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classification</title> <meta name="description" content="Search results for: classification"> <meta name="keywords" content="classification"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="classification"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 1124</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: classification</h1> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1124</span> Classification Influence Index and its Application for k-Nearest Neighbor Classifier</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Sejong%20Oh">Sejong Oh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Classification is an important topic in machine learning and bioinformatics. Many datasets have been introduced for classification tasks. A dataset contains multiple features, and the quality of features influences the classification accuracy of the dataset. The power of classification for each feature differs. In this study, we suggest the Classification Influence Index (CII) as an indicator of classification power for each feature. CII enables evaluation of the features in a dataset and improved classification accuracy by transformation of the dataset. By conducting experiments using CII and the k-nearest neighbor classifier to analyze real datasets, we confirmed that the proposed index provided meaningful improvement of the classification accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=accuracy" title="accuracy">accuracy</a>, <a href="https://publications.waset.org/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/search?q=dataset" title=" dataset"> dataset</a>, <a href="https://publications.waset.org/search?q=data%20preprocessing" title=" data preprocessing"> data preprocessing</a> </p> <a href="https://publications.waset.org/15065/classification-influence-index-and-its-application-for-k-nearest-neighbor-classifier" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/15065/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/15065/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/15065/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/15065/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/15065/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/15065/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/15065/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/15065/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/15065/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/15065/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/15065.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1495</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1123</span> Review and Comparison of Associative Classification Data Mining Approaches</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Suzan%20Wedyan">Suzan Wedyan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Associative classification (AC) is a data mining approach that combines association rule and classification to build classification models (classifiers). AC has attracted a significant attention from several researchers mainly because it derives accurate classifiers that contain simple yet effective rules. In the last decade, a number of associative classification algorithms have been proposed such as Classification based Association (CBA), Classification based on Multiple Association Rules (CMAR), Class based Associative Classification (CACA), and Classification based on Predicted Association Rule (CPAR). This paper surveys major AC algorithms and compares the steps and methods performed in each algorithm including: rule learning, rule sorting, rule pruning, classifier building, and class prediction.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Associative%20Classification" title="Associative Classification">Associative Classification</a>, <a href="https://publications.waset.org/search?q=Classification" title=" Classification"> Classification</a>, <a href="https://publications.waset.org/search?q=Data%20Mining" title=" Data Mining"> Data Mining</a>, <a href="https://publications.waset.org/search?q=Learning" title=" Learning"> Learning</a>, <a href="https://publications.waset.org/search?q=Rule%20Ranking" title=" Rule Ranking"> Rule Ranking</a>, <a href="https://publications.waset.org/search?q=Rule%20Pruning" title=" Rule Pruning"> Rule Pruning</a>, <a href="https://publications.waset.org/search?q=Prediction." title=" Prediction."> Prediction.</a> </p> <a href="https://publications.waset.org/9997152/review-and-comparison-of-associative-classification-data-mining-approaches" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9997152/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9997152/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9997152/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9997152/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9997152/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9997152/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9997152/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9997152/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9997152/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9997152/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9997152.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">6633</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1122</span> Sensitive Analysis of the ZF Model for ABC Multi Criteria Inventory Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Makram%20Ben%20Jeddou">Makram Ben Jeddou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> ABC classification is widely used by managers for inventory control. The classical ABC classification is based on Pareto principle and according to the criterion of the annual use value only. Single criterion classification is often insufficient for a closely inventory control. Multi-criteria inventory classification models have been proposed by researchers in order to consider other important criteria. From these models, we will consider a specific model in order to make a sensitive analysis on the composite score calculated for each item. In fact, this score, based on a normalized average between a good and a bad optimized index, can affect the ABC-item classification. We will focus on items differently assigned to classes and then propose a classification compromise. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=ABC%20classification" title="ABC classification">ABC classification</a>, <a href="https://publications.waset.org/search?q=Multi%20criteria%20inventory%0D%0Aclassification%20models" title=" Multi criteria inventory classification models"> Multi criteria inventory classification models</a>, <a href="https://publications.waset.org/search?q=ZF-model." title=" ZF-model."> ZF-model.</a> </p> <a href="https://publications.waset.org/10001382/sensitive-analysis-of-the-zf-model-for-abc-multi-criteria-inventory-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10001382/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10001382/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10001382/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10001382/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10001382/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10001382/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10001382/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10001382/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10001382/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10001382/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10001382.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2517</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1121</span> A Multiresolution Approach for Noised Texture Classification based on the Co-occurrence Matrix and First Order Statistics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=M.%20Ben%20Othmen">M. Ben Othmen</a>, <a href="https://publications.waset.org/search?q=M.%20Sayadi"> M. Sayadi</a>, <a href="https://publications.waset.org/search?q=F.%20Fnaiech"> F. Fnaiech</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Wavelet transform provides several important characteristics which can be used in a texture analysis and classification. In this work, an efficient texture classification method, which combines concepts from wavelet and co-occurrence matrices, is presented. An Euclidian distance classifier is used to evaluate the various methods of classification. A comparative study is essential to determine the ideal method. Using this conjecture, we developed a novel feature set for texture classification and demonstrate its effectiveness <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Classification" title="Classification">Classification</a>, <a href="https://publications.waset.org/search?q=Wavelet" title=" Wavelet"> Wavelet</a>, <a href="https://publications.waset.org/search?q=Co-occurrence" title=" Co-occurrence"> Co-occurrence</a>, <a href="https://publications.waset.org/search?q=Euclidian%0ADistance" title=" Euclidian Distance"> Euclidian Distance</a>, <a href="https://publications.waset.org/search?q=Classifier" title=" Classifier"> Classifier</a>, <a href="https://publications.waset.org/search?q=Texture." title=" Texture."> Texture.</a> </p> <a href="https://publications.waset.org/4636/a-multiresolution-approach-for-noised-texture-classification-based-on-the-co-occurrence-matrix-and-first-order-statistics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/4636/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/4636/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/4636/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/4636/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/4636/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/4636/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/4636/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/4636/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/4636/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/4636/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/4636.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1481</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1120</span> Classification of Attaks over Cloud Environment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Karim%20Abouelmehdi">Karim Abouelmehdi</a>, <a href="https://publications.waset.org/search?q=Loubna%20Dali"> Loubna Dali</a>, <a href="https://publications.waset.org/search?q=Elmoutaoukkil%20Abdelmajid"> Elmoutaoukkil Abdelmajid</a>, <a href="https://publications.waset.org/search?q=Hoda%20Elsayed%20Eladnani%20Fatiha"> Hoda Elsayed Eladnani Fatiha</a>, <a href="https://publications.waset.org/search?q=Benihssane%20Abderahim"> Benihssane Abderahim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The security of cloud services is the concern of cloud service providers. In this paper, we will mention different classifications of cloud attacks referred by specialized organizations. Each agency has its classification of well-defined properties. The purpose is to present a high-level classification of current research in cloud computing security. This classification is organized around attack strategies and corresponding defenses. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Cloud%20computing" title="Cloud computing">Cloud computing</a>, <a href="https://publications.waset.org/search?q=security" title=" security"> security</a>, <a href="https://publications.waset.org/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/search?q=risk." title=" risk."> risk.</a> </p> <a href="https://publications.waset.org/10001762/classification-of-attaks-over-cloud-environment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10001762/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10001762/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10001762/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10001762/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10001762/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10001762/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10001762/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10001762/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10001762/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10001762/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10001762.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2082</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1119</span> Multi-Label Hierarchical Classification for Protein Function Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Helyane%20B.%20Borges"> Helyane B. Borges</a>, <a href="https://publications.waset.org/search?q=Julio%20Cesar%20Nievola"> Julio Cesar Nievola</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Hierarchical classification is a problem with applications in many areas as protein function prediction where the dates are hierarchically structured. Therefore, it is necessary the development of algorithms able to induce hierarchical classification models. This paper presents experimenters using the algorithm for hierarchical classification called Multi-label Hierarchical Classification using a Competitive Neural Network (MHC-CNN). It was tested in ten datasets the Gene Ontology (GO) Cellular Component Domain. The results are compared with the Clus-HMC and Clus-HSC using the hF-Measure.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Hierarchical%20Classification" title=" Hierarchical Classification"> Hierarchical Classification</a>, <a href="https://publications.waset.org/search?q=Competitive%20Neural%20Network" title=" Competitive Neural Network"> Competitive Neural Network</a>, <a href="https://publications.waset.org/search?q=Global%20Classifier." title=" Global Classifier."> Global Classifier.</a> </p> <a href="https://publications.waset.org/16089/multi-label-hierarchical-classification-for-protein-function-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/16089/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/16089/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/16089/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/16089/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/16089/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/16089/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/16089/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/16089/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/16089/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/16089/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/16089.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2380</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1118</span> Detection and Classification of Power Quality Disturbances Using S-Transform and Wavelet Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Mohamed%20E.%20Salem%20Abozaed">Mohamed E. Salem Abozaed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Detection and classification of power quality (PQ) disturbances is an important consideration to electrical utilities and many industrial customers so that diagnosis and mitigation of such disturbance can be implemented quickly. S-transform algorithm and continuous wavelet transforms (CWT) are time-frequency algorithms, and both of them are powerful in detection and classification of PQ disturbances. This paper presents detection and classification of PQ disturbances using S-transform and CWT algorithms. The results of detection and classification, provides that S-transform is more accurate in detection and classification for most PQ disturbance than CWT algorithm, where as CWT algorithm more powerful in detection in some disturbances like notching <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=CWT" title="CWT">CWT</a>, <a href="https://publications.waset.org/search?q=Disturbances%20classification" title=" Disturbances classification"> Disturbances classification</a>, <a href="https://publications.waset.org/search?q=Disturbances%20detection" title=" Disturbances detection"> Disturbances detection</a>, <a href="https://publications.waset.org/search?q=Power%20quality" title=" Power quality"> Power quality</a>, <a href="https://publications.waset.org/search?q=S-transform." title=" S-transform."> S-transform.</a> </p> <a href="https://publications.waset.org/14779/detection-and-classification-of-power-quality-disturbances-using-s-transform-and-wavelet-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/14779/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/14779/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/14779/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/14779/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/14779/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/14779/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/14779/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/14779/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/14779/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/14779/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/14779.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2599</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1117</span> GA Based Optimal Feature Extraction Method for Functional Data Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Jun%20Wan">Jun Wan</a>, <a href="https://publications.waset.org/search?q=Zehua%20Chen"> Zehua Chen</a>, <a href="https://publications.waset.org/search?q=Yingwu%20Chen"> Yingwu Chen</a>, <a href="https://publications.waset.org/search?q=Zhidong%20Bai"> Zhidong Bai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Classification is an interesting problem in functional data analysis (FDA), because many science and application problems end up with classification problems, such as recognition, prediction, control, decision making, management, etc. As the high dimension and high correlation in functional data (FD), it is a key problem to extract features from FD whereas keeping its global characters, which relates to the classification efficiency and precision to heavens. In this paper, a novel automatic method which combined Genetic Algorithm (GA) and classification algorithm to extract classification features is proposed. In this method, the optimal features and classification model are approached via evolutional study step by step. It is proved by theory analysis and experiment test that this method has advantages in improving classification efficiency, precision and robustness whereas using less features and the dimension of extracted classification features can be controlled. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Classification" title="Classification">Classification</a>, <a href="https://publications.waset.org/search?q=functional%20data" title=" functional data"> functional data</a>, <a href="https://publications.waset.org/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a>, <a href="https://publications.waset.org/search?q=genetic%20algorithm" title="genetic algorithm">genetic algorithm</a>, <a href="https://publications.waset.org/search?q=wavelet." title=" wavelet."> wavelet.</a> </p> <a href="https://publications.waset.org/13723/ga-based-optimal-feature-extraction-method-for-functional-data-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/13723/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/13723/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/13723/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/13723/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/13723/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/13723/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/13723/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/13723/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/13723/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/13723/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/13723.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1555</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1116</span> Meta-Classification using SVM Classifiers for Text Documents</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Daniel%20I.%20Morariu">Daniel I. Morariu</a>, <a href="https://publications.waset.org/search?q=Lucian%20N.%20Vintan"> Lucian N. Vintan</a>, <a href="https://publications.waset.org/search?q=Volker%20Tresp"> Volker Tresp</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Text categorization is the problem of classifying text documents into a set of predefined classes. In this paper, we investigated three approaches to build a meta-classifier in order to increase the classification accuracy. The basic idea is to learn a metaclassifier to optimally select the best component classifier for each data point. The experimental results show that combining classifiers can significantly improve the accuracy of classification and that our meta-classification strategy gives better results than each individual classifier. For 7083 Reuters text documents we obtained a classification accuracies up to 92.04%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Meta-classification" title="Meta-classification">Meta-classification</a>, <a href="https://publications.waset.org/search?q=Learning%20with%20Kernels" title=" Learning with Kernels"> Learning with Kernels</a>, <a href="https://publications.waset.org/search?q=Support%0AVector%20Machine" title=" Support Vector Machine"> Support Vector Machine</a>, <a href="https://publications.waset.org/search?q=and%20Performance%20Evaluation." title=" and Performance Evaluation."> and Performance Evaluation.</a> </p> <a href="https://publications.waset.org/5749/meta-classification-using-svm-classifiers-for-text-documents" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/5749/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/5749/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/5749/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/5749/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/5749/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/5749/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/5749/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/5749/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/5749/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/5749/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/5749.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1616</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1115</span> Meta-Learning for Hierarchical Classification and Applications in Bioinformatics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Fabio%20Fabris">Fabio Fabris</a>, <a href="https://publications.waset.org/search?q=Alex%20A.%20Freitas"> Alex A. Freitas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Hierarchical classification is a special type of classification task where the class labels are organised into a hierarchy, with more generic class labels being ancestors of more specific ones. Meta-learning for classification-algorithm recommendation consists of recommending to the user a classification algorithm, from a pool of candidate algorithms, for a dataset, based on the past performance of the candidate algorithms in other datasets. Meta-learning is normally used in conventional, non-hierarchical classification. By contrast, this paper proposes a meta-learning approach for more challenging task of hierarchical classification, and evaluates it in a large number of bioinformatics datasets. Hierarchical classification is especially relevant for bioinformatics problems, as protein and gene functions tend to be organised into a hierarchy of class labels. This work proposes meta-learning approach for recommending the best hierarchical classification algorithm to a hierarchical classification dataset. This work’s contributions are: 1) proposing an algorithm for splitting hierarchical datasets into new datasets to increase the number of meta-instances, 2) proposing meta-features for hierarchical classification, and 3) interpreting decision-tree meta-models for hierarchical classification algorithm recommendation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Algorithm%20recommendation" title="Algorithm recommendation">Algorithm recommendation</a>, <a href="https://publications.waset.org/search?q=meta-learning" title=" meta-learning"> meta-learning</a>, <a href="https://publications.waset.org/search?q=bioinformatics" title=" bioinformatics"> bioinformatics</a>, <a href="https://publications.waset.org/search?q=hierarchical%20classification." title=" hierarchical classification."> hierarchical classification.</a> </p> <a href="https://publications.waset.org/10009269/meta-learning-for-hierarchical-classification-and-applications-in-bioinformatics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10009269/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10009269/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10009269/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10009269/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10009269/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10009269/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10009269/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10009269/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10009269/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10009269/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10009269.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1370</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1114</span> Binary Classification Tree with Tuned Observation-based Clustering</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Maythapolnun%20Athimethphat">Maythapolnun Athimethphat</a>, <a href="https://publications.waset.org/search?q=Boontarika%20Lerteerawong"> Boontarika Lerteerawong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>There are several approaches for handling multiclass classification. Aside from one-against-one (OAO) and one-against-all (OAA), hierarchical classification technique is also commonly used. A binary classification tree is a hierarchical classification structure that breaks down a k-class problem into binary sub-problems, each solved by a binary classifier. In each node, a set of classes is divided into two subsets. A good class partition should be able to group similar classes together. Many algorithms measure similarity in term of distance between class centroids. Classes are grouped together by a clustering algorithm when distances between their centroids are small. In this paper, we present a binary classification tree with tuned observation-based clustering (BCT-TOB) that finds a class partition by performing clustering on observations instead of class centroids. A merging step is introduced to merge any insignificant class split. The experiment shows that performance of BCT-TOB is comparable to other algorithms.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=multiclass%20classification" title="multiclass classification">multiclass classification</a>, <a href="https://publications.waset.org/search?q=hierarchical%20classification" title=" hierarchical classification"> hierarchical classification</a>, <a href="https://publications.waset.org/search?q=binary%20classification%20tree" title=" binary classification tree"> binary classification tree</a>, <a href="https://publications.waset.org/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/search?q=observation-based%20clustering" title=" observation-based clustering"> observation-based clustering</a> </p> <a href="https://publications.waset.org/2571/binary-classification-tree-with-tuned-observation-based-clustering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/2571/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/2571/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/2571/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/2571/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/2571/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/2571/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/2571/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/2571/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/2571/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/2571/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/2571.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1732</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1113</span> Pose Normalization Network for Object Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Bingquan%20Shen">Bingquan Shen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Convolutional Neural Networks (CNN) have demonstrated their effectiveness in synthesizing 3D views of object instances at various viewpoints. Given the problem where one have limited viewpoints of a particular object for classification, we present a pose normalization architecture to transform the object to existing viewpoints in the training dataset before classification to yield better classification performance. We have demonstrated that this Pose Normalization Network (PNN) can capture the style of the target object and is able to re-render it to a desired viewpoint. Moreover, we have shown that the PNN improves the classification result for the 3D chairs dataset and ShapeNet airplanes dataset when given only images at limited viewpoint, as compared to a CNN baseline. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Convolutional%20neural%20networks" title="Convolutional neural networks">Convolutional neural networks</a>, <a href="https://publications.waset.org/search?q=object%20classification" title=" object classification"> object classification</a>, <a href="https://publications.waset.org/search?q=pose%20normalization" title=" pose normalization"> pose normalization</a>, <a href="https://publications.waset.org/search?q=viewpoint%20invariant." title=" viewpoint invariant."> viewpoint invariant.</a> </p> <a href="https://publications.waset.org/10006613/pose-normalization-network-for-object-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10006613/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10006613/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10006613/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10006613/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10006613/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10006613/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10006613/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10006613/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10006613/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10006613/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10006613.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1120</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1112</span> Lean Models Classification: Towards a Holistic View</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Y.%20Tiamaz">Y. Tiamaz</a>, <a href="https://publications.waset.org/search?q=N.%20Souissi"> N. Souissi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>The purpose of this paper is to present a classification of Lean models which aims to capture all the concepts related to this approach and thus facilitate its implementation. This classification allows the identification of the most relevant models according to several dimensions. From this perspective, we present a review and an analysis of Lean models literature and we propose dimensions for the classification of the current proposals while respecting among others the axes of the Lean approach, the maturity of the models as well as their application domains. This classification allowed us to conclude that researchers essentially consider the Lean approach as a toolbox also they design their models to solve problems related to a specific environment. Since Lean approach is no longer intended only for the automotive sector where it was invented, but to all fields (IT, Hospital, ...), we consider that this approach requires a generic model that is capable of being implemented in all areas.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Lean%20approach" title="Lean approach">Lean approach</a>, <a href="https://publications.waset.org/search?q=lean%20models" title=" lean models"> lean models</a>, <a href="https://publications.waset.org/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/search?q=dimensions" title=" dimensions"> dimensions</a>, <a href="https://publications.waset.org/search?q=holistic%20view." title=" holistic view."> holistic view.</a> </p> <a href="https://publications.waset.org/10006885/lean-models-classification-towards-a-holistic-view" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10006885/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10006885/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10006885/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10006885/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10006885/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10006885/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10006885/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10006885/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10006885/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10006885/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10006885.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1248</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1111</span> Obstacle Classification Method Based On 2D LIDAR Database</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Moohyun%20Lee">Moohyun Lee</a>, <a href="https://publications.waset.org/search?q=Soojung%20Hur"> Soojung Hur</a>, <a href="https://publications.waset.org/search?q=Yongwan%20Park"> Yongwan Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>We propose obstacle classification method based on 2D LIDAR Database. The existing obstacle classification method based on 2D LIDAR, has an advantage in terms of accuracy and shorter calculation time. However, it was difficult to classifier the type of obstacle and therefore accurate path planning was not possible. In order to overcome this problem, a method of classifying obstacle type based on width data of obstacle was proposed. However, width data was not sufficient to improve accuracy. In this paper, database was established by width and intensity data; the first classification was processed by the width data; the second classification was processed by the intensity data; classification was processed by comparing to database; result of obstacle classification was determined by finding the one with highest similarity values. An experiment using an actual autonomous vehicle under real environment shows that calculation time declined in comparison to 3D LIDAR and it was possible to classify obstacle using single 2D LIDAR.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Obstacle" title="Obstacle">Obstacle</a>, <a href="https://publications.waset.org/search?q=Classification" title=" Classification"> Classification</a>, <a href="https://publications.waset.org/search?q=LIDAR" title=" LIDAR"> LIDAR</a>, <a href="https://publications.waset.org/search?q=Segmentation" title=" Segmentation"> Segmentation</a>, <a href="https://publications.waset.org/search?q=Width" title=" Width"> Width</a>, <a href="https://publications.waset.org/search?q=Intensity" title=" Intensity"> Intensity</a>, <a href="https://publications.waset.org/search?q=Database." title=" Database."> Database.</a> </p> <a href="https://publications.waset.org/9999089/obstacle-classification-method-based-on-2d-lidar-database" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9999089/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9999089/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9999089/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9999089/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9999089/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9999089/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9999089/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9999089/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9999089/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9999089/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9999089.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">3445</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1110</span> An Efficient Obstacle Detection Algorithm Using Colour and Texture</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Chau%20Nguyen%20Viet">Chau Nguyen Viet</a>, <a href="https://publications.waset.org/search?q=Ian%20Marshall"> Ian Marshall</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>This paper presents a new classification algorithm using colour and texture for obstacle detection. Colour information is computationally cheap to learn and process. However in many cases, colour alone does not provide enough information for classification. Texture information can improve classification performance but usually comes at an expensive cost. Our algorithm uses both colour and texture features but texture is only needed when colour is unreliable. During the training stage, texture features are learned specifically to improve the performance of a colour classifier. The algorithm learns a set of simple texture features and only the most effective features are used in the classification stage. Therefore our algorithm has a very good classification rate while is still fast enough to run on a limited computer platform. The proposed algorithm was tested with a challenging outdoor image set. Test result shows the algorithm achieves a much better trade-off between classification performance and efficiency than a typical colour classifier.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Colour" title="Colour">Colour</a>, <a href="https://publications.waset.org/search?q=texture" title=" texture"> texture</a>, <a href="https://publications.waset.org/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/search?q=obstacle%20detection." title=" obstacle detection."> obstacle detection.</a> </p> <a href="https://publications.waset.org/1945/an-efficient-obstacle-detection-algorithm-using-colour-and-texture" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/1945/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/1945/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/1945/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/1945/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/1945/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/1945/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/1945/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/1945/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/1945/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/1945/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/1945.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1823</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1109</span> Improving Classification in Bayesian Networks using Structural Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Hong%20Choon%20Ong">Hong Choon Ong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Na茂ve Bayes classifiers are simple probabilistic classifiers. Classification extracts patterns by using data file with a set of labeled training examples and is currently one of the most significant areas in data mining. However, Na茂ve Bayes assumes the independence among the features. Structural learning among the features thus helps in the classification problem. In this study, the use of structural learning in Bayesian Network is proposed to be applied where there are relationships between the features when using the Na茂ve Bayes. The improvement in the classification using structural learning is shown if there exist relationship between the features or when they are not independent. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Bayesian%20Network" title="Bayesian Network">Bayesian Network</a>, <a href="https://publications.waset.org/search?q=Classification" title=" Classification"> Classification</a>, <a href="https://publications.waset.org/search?q=Na%C3%AFve%20Bayes" title=" Na茂ve Bayes"> Na茂ve Bayes</a>, <a href="https://publications.waset.org/search?q=Structural%20Learning." title="Structural Learning.">Structural Learning.</a> </p> <a href="https://publications.waset.org/15047/improving-classification-in-bayesian-networks-using-structural-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/15047/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/15047/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/15047/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/15047/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/15047/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/15047/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/15047/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/15047/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/15047/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/15047/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/15047.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2599</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1108</span> A Novel Approach for Protein Classification Using Fourier Transform</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=A.%20F.%20Ali">A. F. Ali</a>, <a href="https://publications.waset.org/search?q=D.%20M.%20Shawky"> D. M. Shawky</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Discovering new biological knowledge from the highthroughput biological data is a major challenge to bioinformatics today. To address this challenge, we developed a new approach for protein classification. Proteins that are evolutionarily- and thereby functionally- related are said to belong to the same classification. Identifying protein classification is of fundamental importance to document the diversity of the known protein universe. It also provides a means to determine the functional roles of newly discovered protein sequences. Our goal is to predict the functional classification of novel protein sequences based on a set of features extracted from each protein sequence. The proposed technique used datasets extracted from the Structural Classification of Proteins (SCOP) database. A set of spectral domain features based on Fast Fourier Transform (FFT) is used. The proposed classifier uses multilayer back propagation (MLBP) neural network for protein classification. The maximum classification accuracy is about 91% when applying the classifier to the full four levels of the SCOP database. However, it reaches a maximum of 96% when limiting the classification to the family level. The classification results reveal that spectral domain contains information that can be used for classification with high accuracy. In addition, the results emphasize that sequence similarity measures are of great importance especially at the family level.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Bioinformatics" title="Bioinformatics">Bioinformatics</a>, <a href="https://publications.waset.org/search?q=Artificial%20Neural%20Networks" title=" Artificial Neural Networks"> Artificial Neural Networks</a>, <a href="https://publications.waset.org/search?q=Protein%20Sequence%20Analysis" title=" Protein Sequence Analysis"> Protein Sequence Analysis</a>, <a href="https://publications.waset.org/search?q=Feature%20Extraction." title=" Feature Extraction."> Feature Extraction.</a> </p> <a href="https://publications.waset.org/7898/a-novel-approach-for-protein-classification-using-fourier-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/7898/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/7898/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/7898/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/7898/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/7898/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/7898/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/7898/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/7898/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/7898/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/7898/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/7898.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2360</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1107</span> Vehicle Type Classification with Geometric and Appearance Attributes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Ghada%20S.%20Moussa">Ghada S. Moussa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>With the increase in population along with economic prosperity, an enormous increase in the number and types of vehicles on the roads occurred. This fact brings a growing need for efficiently yet effectively classifying vehicles into their corresponding categories, which play a crucial role in many areas of infrastructure planning and traffic management.</p> <p>This paper presents two vehicle-type classification approaches; 1) geometric-based and 2) appearance-based. The two classification approaches are used for two tasks: multi-class and intra-class vehicle classifications. For the evaluation purpose of the proposed classification approaches’ performance and the identification of the most effective yet efficient one, 10-fold cross-validation technique is used with a large dataset. The proposed approaches are distinguishable from previous research on vehicle classification in which: i) they consider both geometric and appearance attributes of vehicles, and ii) they perform remarkably well in both multi-class and intra-class vehicle classification. Experimental results exhibit promising potentials implementations of the proposed vehicle classification approaches into real-world applications.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Appearance%20attributes" title="Appearance attributes">Appearance attributes</a>, <a href="https://publications.waset.org/search?q=Geometric%20attributes" title=" Geometric attributes"> Geometric attributes</a>, <a href="https://publications.waset.org/search?q=Support%20vector%20machine" title=" Support vector machine"> Support vector machine</a>, <a href="https://publications.waset.org/search?q=Vehicle%20classification." title=" Vehicle classification."> Vehicle classification.</a> </p> <a href="https://publications.waset.org/9997770/vehicle-type-classification-with-geometric-and-appearance-attributes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9997770/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9997770/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9997770/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9997770/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9997770/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9997770/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9997770/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9997770/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9997770/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9997770/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9997770.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">4278</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1106</span> Wavelet and K-L Seperability Based Feature Extraction Method for Functional Data Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Jun%20Wan">Jun Wan</a>, <a href="https://publications.waset.org/search?q=Zehua%20Chen"> Zehua Chen</a>, <a href="https://publications.waset.org/search?q=Yingwu%20Chen"> Yingwu Chen</a>, <a href="https://publications.waset.org/search?q=Zhidong%20Bai"> Zhidong Bai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes a novel feature extraction method, based on Discrete Wavelet Transform (DWT) and K-L Seperability (KLS), for the classification of Functional Data (FD). This method combines the decorrelation and reduction property of DWT and the additive independence property of KLS, which is helpful to extraction classification features of FD. It is an advanced approach of the popular wavelet based shrinkage method for functional data reduction and classification. A theory analysis is given in the paper to prove the consistent convergence property, and a simulation study is also done to compare the proposed method with the former shrinkage ones. The experiment results show that this method has advantages in improving classification efficiency, precision and robustness. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=classification" title="classification">classification</a>, <a href="https://publications.waset.org/search?q=functional%20data" title=" functional data"> functional data</a>, <a href="https://publications.waset.org/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a>, <a href="https://publications.waset.org/search?q=K-Lseperability" title=" K-Lseperability"> K-Lseperability</a>, <a href="https://publications.waset.org/search?q=wavelet." title=" wavelet."> wavelet.</a> </p> <a href="https://publications.waset.org/6083/wavelet-and-k-l-seperability-based-feature-extraction-method-for-functional-data-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/6083/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/6083/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/6083/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/6083/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/6083/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/6083/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/6083/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/6083/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/6083/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/6083/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/6083.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1466</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1105</span> Classification of Non Stationary Signals Using Ben Wavelet and Artificial Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Mohammed%20Benbrahim">Mohammed Benbrahim</a>, <a href="https://publications.waset.org/search?q=Khalid%20Benjelloun"> Khalid Benjelloun</a>, <a href="https://publications.waset.org/search?q=Aomar%20Ibenbrahim"> Aomar Ibenbrahim</a>, <a href="https://publications.waset.org/search?q=Adil%20Daoudi"> Adil Daoudi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>The automatic classification of non stationary signals is an important practical goal in several domains. An essential classification task is to allocate the incoming signal to a group associated with the kind of physical phenomena producing it. In this paper, we present a modular system composed by three blocs: 1) Representation, 2) Dimensionality reduction and 3) Classification. The originality of our work consists in the use of a new wavelet called "Ben wavelet" in the representation stage. For the dimensionality reduction, we propose a new algorithm based on the random projection and the principal component analysis.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Seismic%20signals" title="Seismic signals">Seismic signals</a>, <a href="https://publications.waset.org/search?q=Ben%20Wavelet" title=" Ben Wavelet"> Ben Wavelet</a>, <a href="https://publications.waset.org/search?q=Dimensionality%0D%0Areduction" title=" Dimensionality reduction"> Dimensionality reduction</a>, <a href="https://publications.waset.org/search?q=Artificial%20neural%20networks" title=" Artificial neural networks"> Artificial neural networks</a>, <a href="https://publications.waset.org/search?q=Classification." title=" Classification."> Classification.</a> </p> <a href="https://publications.waset.org/13996/classification-of-non-stationary-signals-using-ben-wavelet-and-artificial-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/13996/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/13996/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/13996/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/13996/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/13996/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/13996/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/13996/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/13996/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/13996/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/13996/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/13996.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1450</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1104</span> An Amalgam Approach for DICOM Image Classification and Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=J.%20Umamaheswari">J. Umamaheswari</a>, <a href="https://publications.waset.org/search?q=G.%20Radhamani"> G. Radhamani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>This paper describes about the process of recognition and classification of brain images such as normal and abnormal based on PSO-SVM. Image Classification is becoming more important for medical diagnosis process. In medical area especially for diagnosis the abnormality of the patient is classified, which plays a great role for the doctors to diagnosis the patient according to the severeness of the diseases. In case of DICOM images it is very tough for optimal recognition and early detection of diseases. Our work focuses on recognition and classification of DICOM image based on collective approach of digital image processing. For optimal recognition and classification Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Support Vector Machine (SVM) are used. The collective approach by using PSO-SVM gives high approximation capability and much faster convergence.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Recognition" title="Recognition">Recognition</a>, <a href="https://publications.waset.org/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/search?q=Relaxed%20Median%20Filter" title=" Relaxed Median Filter"> Relaxed Median Filter</a>, <a href="https://publications.waset.org/search?q=Adaptive%20thresholding" title=" Adaptive thresholding"> Adaptive thresholding</a>, <a href="https://publications.waset.org/search?q=clustering%20and%20Neural%20Networks" title=" clustering and Neural Networks"> clustering and Neural Networks</a> </p> <a href="https://publications.waset.org/13370/an-amalgam-approach-for-dicom-image-classification-and-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/13370/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/13370/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/13370/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/13370/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/13370/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/13370/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/13370/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/13370/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/13370/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/13370/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/13370.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2259</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1103</span> Improving Classification Accuracy with Discretization on Datasets Including Continuous Valued Features</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Mehmet%20Hacibeyoglu">Mehmet Hacibeyoglu</a>, <a href="https://publications.waset.org/search?q=Ahmet%20Arslan"> Ahmet Arslan</a>, <a href="https://publications.waset.org/search?q=Sirzat%20Kahramanli"> Sirzat Kahramanli</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study analyzes the effect of discretization on classification of datasets including continuous valued features. Six datasets from UCI which containing continuous valued features are discretized with entropy-based discretization method. The performance improvement between the dataset with original features and the dataset with discretized features is compared with k-nearest neighbors, Naive Bayes, C4.5 and CN2 data mining classification algorithms. As the result the classification accuracies of the six datasets are improved averagely by 1.71% to 12.31%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Data%20mining%20classification%20algorithms" title="Data mining classification algorithms">Data mining classification algorithms</a>, <a href="https://publications.waset.org/search?q=entropy-baseddiscretization%20method" title=" entropy-baseddiscretization method"> entropy-baseddiscretization method</a> </p> <a href="https://publications.waset.org/1314/improving-classification-accuracy-with-discretization-on-datasets-including-continuous-valued-features" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/1314/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/1314/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/1314/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/1314/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/1314/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/1314/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/1314/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/1314/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/1314/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/1314/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/1314.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2461</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1102</span> Computer-aided Lenke Classification of Scoliotic Spines</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Neila%20Mezghani">Neila Mezghani</a>, <a href="https://publications.waset.org/search?q=Philippe%20Phan"> Philippe Phan</a>, <a href="https://publications.waset.org/search?q=Hubert%20Labelle"> Hubert Labelle</a>, <a href="https://publications.waset.org/search?q=Carl%20Eric%20Aubin"> Carl Eric Aubin</a>, <a href="https://publications.waset.org/search?q=Jacques%20de%20Guise"> Jacques de Guise</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>The identification and classification of the spine deformity play an important role when considering surgical planning for adolescent patients with idiopathic scoliosis. The subject of this article is the Lenke classification of scoliotic spines using Cobb angle measurements. The purpose is two-fold: (1) design a rulebased diagram to assist clinicians in the classification process and (2) investigate a computer classifier which improves the classification time and accuracy. The rule-based diagram efficiency was evaluated in a series of scoliotic classifications by 10 clinicians. The computer classifier was tested on a radiographic measurement database of 603 patients. Classification accuracy was 93% using the rule-based diagram and 99% for the computer classifier. Both the computer classifier and the rule based diagram can efficiently assist clinicians in their Lenke classification of spine scoliosis.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Scoliosis" title="Scoliosis">Scoliosis</a>, <a href="https://publications.waset.org/search?q=Lenke%20model" title=" Lenke model"> Lenke model</a>, <a href="https://publications.waset.org/search?q=decision-rules" title=" decision-rules"> decision-rules</a>, <a href="https://publications.waset.org/search?q=computer%20aided%20classifier." title=" computer aided classifier."> computer aided classifier.</a> </p> <a href="https://publications.waset.org/3958/computer-aided-lenke-classification-of-scoliotic-spines" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/3958/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/3958/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/3958/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/3958/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/3958/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/3958/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/3958/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/3958/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/3958/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/3958/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/3958.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1636</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1101</span> Dataset Analysis Using Membership-Deviation Graph</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Itgel%20Bayarsaikhan">Itgel Bayarsaikhan</a>, <a href="https://publications.waset.org/search?q=Jimin%20Lee"> Jimin Lee</a>, <a href="https://publications.waset.org/search?q=Sejong%20Oh"> Sejong Oh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Classification is one of the primary themes in computational biology. The accuracy of classification strongly depends on quality of a dataset, and we need some method to evaluate this quality. In this paper, we propose a new graphical analysis method using 'Membership-Deviation Graph (MDG)' for analyzing quality of a dataset. MDG represents degree of membership and deviations for instances of a class in the dataset. The result of MDG analysis is used for understanding specific feature and for selecting best feature for classification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=feature" title="feature">feature</a>, <a href="https://publications.waset.org/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/search?q=machine%20learning%20algorithm." title=" machine learning algorithm."> machine learning algorithm.</a> </p> <a href="https://publications.waset.org/4575/dataset-analysis-using-membership-deviation-graph" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/4575/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/4575/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/4575/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/4575/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/4575/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/4575/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/4575/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/4575/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/4575/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/4575/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/4575.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1445</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1100</span> Unsupervised Texture Classification and Segmentation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=V.P.Subramanyam%20Rallabandi">V.P.Subramanyam Rallabandi</a>, <a href="https://publications.waset.org/search?q=S.K.Sett"> S.K.Sett</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An unsupervised classification algorithm is derived by modeling observed data as a mixture of several mutually exclusive classes that are each described by linear combinations of independent non-Gaussian densities. The algorithm estimates the data density in each class by using parametric nonlinear functions that fit to the non-Gaussian structure of the data. This improves classification accuracy compared with standard Gaussian mixture models. When applied to textures, the algorithm can learn basis functions for images that capture the statistically significant structure intrinsic in the images. We apply this technique to the problem of unsupervised texture classification and segmentation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Gaussian%20Mixture%20Model" title="Gaussian Mixture Model">Gaussian Mixture Model</a>, <a href="https://publications.waset.org/search?q=Independent%20Component%0AAnalysis" title=" Independent Component Analysis"> Independent Component Analysis</a>, <a href="https://publications.waset.org/search?q=Segmentation" title=" Segmentation"> Segmentation</a>, <a href="https://publications.waset.org/search?q=Unsupervised%20Classification." title=" Unsupervised Classification."> Unsupervised Classification.</a> </p> <a href="https://publications.waset.org/4391/unsupervised-texture-classification-and-segmentation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/4391/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/4391/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/4391/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/4391/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/4391/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/4391/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/4391/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/4391/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/4391/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/4391/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/4391.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1591</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1099</span> WebGD: A CORBA-based Document Classification and Retrieval System on the Web</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Fuyang%20Peng">Fuyang Peng</a>, <a href="https://publications.waset.org/search?q=Bo%20Deng"> Bo Deng</a>, <a href="https://publications.waset.org/search?q=Chao%20Qi"> Chao Qi</a>, <a href="https://publications.waset.org/search?q=Mou%20Zhan"> Mou Zhan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the design and implementation of the WebGD, a CORBA-based document classification and retrieval system on Internet. The WebGD makes use of such techniques as Web, CORBA, Java, NLP, fuzzy technique, knowledge-based processing and database technology. Unified classification and retrieval model, classifying and retrieving with one reasoning engine and flexible working mode configuration are some of its main features. The architecture of WebGD, the unified classification and retrieval model, the components of the WebGD server and the fuzzy inference engine are discussed in this paper in detail. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Text%20Mining" title="Text Mining">Text Mining</a>, <a href="https://publications.waset.org/search?q=document%20classification" title=" document classification"> document classification</a>, <a href="https://publications.waset.org/search?q=knowledgeprocessing" title=" knowledgeprocessing"> knowledgeprocessing</a>, <a href="https://publications.waset.org/search?q=fuzzy%20logic" title=" fuzzy logic"> fuzzy logic</a>, <a href="https://publications.waset.org/search?q=Web" title=" Web"> Web</a>, <a href="https://publications.waset.org/search?q=CORBA" title=" CORBA"> CORBA</a> </p> <a href="https://publications.waset.org/7676/webgd-a-corba-based-document-classification-and-retrieval-system-on-the-web" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/7676/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/7676/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/7676/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/7676/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/7676/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/7676/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/7676/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/7676/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/7676/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/7676/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/7676.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1848</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1098</span> Performance Analysis of Artificial Neural Network Based Land Cover Classification </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Najam%20Aziz">Najam Aziz</a>, <a href="https://publications.waset.org/search?q=Nasru%20Minallah"> Nasru Minallah</a>, <a href="https://publications.waset.org/search?q=Ahmad%20Junaid"> Ahmad Junaid</a>, <a href="https://publications.waset.org/search?q=Kashaf%20Gul"> Kashaf Gul </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Landcover classification using automated classification techniques, while employing remotely sensed multi-spectral imagery, is one of the promising areas of research. Different land conditions at different time are captured through satellite and monitored by applying different classification algorithms in specific environment. In this paper, a SPOT-5 image provided by SUPARCO has been studied and classified in Environment for Visual Interpretation (ENVI), a tool widely used in remote sensing. Then, Artificial Neural Network (ANN) classification technique is used to detect the land cover changes in Abbottabad district. Obtained results are compared with a pixel based Distance classifier. The results show that ANN gives the better overall accuracy of 99.20% and Kappa coefficient value of 0.98 over the Mahalanobis Distance Classifier. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Landcover%20classification" title="Landcover classification">Landcover classification</a>, <a href="https://publications.waset.org/search?q=artificial%20neural%20network" title=" artificial neural network"> artificial neural network</a>, <a href="https://publications.waset.org/search?q=remote%20sensing" title=" remote sensing"> remote sensing</a>, <a href="https://publications.waset.org/search?q=SPOT-5." title=" SPOT-5."> SPOT-5.</a> </p> <a href="https://publications.waset.org/10007011/performance-analysis-of-artificial-neural-network-based-land-cover-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10007011/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10007011/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10007011/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10007011/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10007011/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10007011/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10007011/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10007011/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10007011/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10007011/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10007011.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1606</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1097</span> Genetic Programming Approach for Multi-Category Pattern Classification Appliedto Network Intrusions Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=K.M.%20Faraoun">K.M. Faraoun</a>, <a href="https://publications.waset.org/search?q=A.%20Boukelif"> A. Boukelif</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes a new approach of classification using genetic programming. The proposed technique consists of genetically coevolving a population of non-linear transformations on the input data to be classified, and map them to a new space with a reduced dimension, in order to get a maximum inter-classes discrimination. The classification of new samples is then performed on the transformed data, and so become much easier. Contrary to the existing GP-classification techniques, the proposed one use a dynamic repartition of the transformed data in separated intervals, the efficacy of a given intervals repartition is handled by the fitness criterion, with a maximum classes discrimination. Experiments were first performed using the Fisher-s Iris dataset, and then, the KDD-99 Cup dataset was used to study the intrusion detection and classification problem. Obtained results demonstrate that the proposed genetic approach outperform the existing GP-classification methods [1],[2] and [3], and give a very accepted results compared to other existing techniques proposed in [4],[5],[6],[7] and [8]. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Genetic%20programming" title="Genetic programming">Genetic programming</a>, <a href="https://publications.waset.org/search?q=patterns%20classification" title=" patterns classification"> patterns classification</a>, <a href="https://publications.waset.org/search?q=intrusion%20detection" title="intrusion detection">intrusion detection</a> </p> <a href="https://publications.waset.org/1315/genetic-programming-approach-for-multi-category-pattern-classification-appliedto-network-intrusions-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/1315/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/1315/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/1315/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/1315/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/1315/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/1315/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/1315/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/1315/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/1315/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/1315/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/1315.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1711</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1096</span> Feature Selection for Web Page Classification Using Swarm Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=B.%20Leela%20Devi">B. Leela Devi</a>, <a href="https://publications.waset.org/search?q=A.%20Sankar"> A. Sankar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>The web’s increased popularity has included a huge amount of information, due to which automated web page classification systems are essential to improve search engines’ performance. Web pages have many features like HTML or XML tags, hyperlinks, URLs and text contents which can be considered during an automated classification process. It is known that Webpage classification is enhanced by hyperlinks as it reflects Web page linkages. The aim of this study is to reduce the number of features to be used to improve the accuracy of the classification of web pages. In this paper, a novel feature selection method using an improved Particle Swarm Optimization (PSO) using principle of evolution is proposed. The extracted features were tested on the WebKB dataset using a parallel Neural Network to reduce the computational cost.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Web%20page%20classification" title="Web page classification">Web page classification</a>, <a href="https://publications.waset.org/search?q=WebKB%20Dataset" title=" WebKB Dataset"> WebKB Dataset</a>, <a href="https://publications.waset.org/search?q=Term%0D%0AFrequency-Inverse%20Document%20Frequency%20%28TF-IDF%29" title=" Term Frequency-Inverse Document Frequency (TF-IDF)"> Term Frequency-Inverse Document Frequency (TF-IDF)</a>, <a href="https://publications.waset.org/search?q=Particle%20Swarm%0D%0AOptimization%20%28PSO%29." title=" Particle Swarm Optimization (PSO)."> Particle Swarm Optimization (PSO).</a> </p> <a href="https://publications.waset.org/10000701/feature-selection-for-web-page-classification-using-swarm-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10000701/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10000701/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10000701/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10000701/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10000701/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10000701/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10000701/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10000701/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10000701/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10000701/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10000701.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">3259</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1095</span> Lithofacies Classification from Well Log Data Using Neural Networks, Interval Neutrosophic Sets and Quantification of Uncertainty</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Pawalai%20Kraipeerapun">Pawalai Kraipeerapun</a>, <a href="https://publications.waset.org/search?q=Chun%20Che%20Fung"> Chun Che Fung</a>, <a href="https://publications.waset.org/search?q=Kok%20Wai%20Wong"> Kok Wai Wong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>This paper proposes a novel approach to the question of lithofacies classification based on an assessment of the uncertainty in the classification results. The proposed approach has multiple neural networks (NN), and interval neutrosophic sets (INS) are used to classify the input well log data into outputs of multiple classes of lithofacies. A pair of n-class neural networks are used to predict n-degree of truth memberships and n-degree of false memberships. Indeterminacy memberships or uncertainties in the predictions are estimated using a multidimensional interpolation method. These three memberships form the INS used to support the confidence in results of multiclass classification. Based on the experimental data, our approach improves the classification performance as compared to an existing technique applied only to the truth membership. In addition, our approach has the capability to provide a measure of uncertainty in the problem of multiclass classification.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Multiclass%20classification" title="Multiclass classification">Multiclass classification</a>, <a href="https://publications.waset.org/search?q=feed-forward%20backpropagation%0D%0Aneural%20network" title=" feed-forward backpropagation neural network"> feed-forward backpropagation neural network</a>, <a href="https://publications.waset.org/search?q=interval%20neutrosophic%20sets" title=" interval neutrosophic sets"> interval neutrosophic sets</a>, <a href="https://publications.waset.org/search?q=uncertainty." title=" uncertainty."> uncertainty.</a> </p> <a href="https://publications.waset.org/13852/lithofacies-classification-from-well-log-data-using-neural-networks-interval-neutrosophic-sets-and-quantification-of-uncertainty" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/13852/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/13852/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/13852/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/13852/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/13852/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/13852/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/13852/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/13852/ris" target="_blank" rel="nofollow" class="btn 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