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tree</title> <meta name="description" content="Search results for: decision tree"> <meta name="keywords" content="decision tree"> <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 navbar-toggler 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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="decision tree"> <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> 4672</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: decision tree</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4672</span> A Ratio-Weighted Decision Tree Algorithm for Imbalance Dataset Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Doyin%20Afolabi">Doyin Afolabi</a>, <a href="https://publications.waset.org/abstracts/search?q=Phillip%20Adewole"> Phillip Adewole</a>, <a href="https://publications.waset.org/abstracts/search?q=Oladipupo%20Sennaike"> Oladipupo Sennaike</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Most well-known classifiers, including the decision tree algorithm, can make predictions on balanced datasets efficiently. However, the decision tree algorithm tends to be biased towards imbalanced datasets because of the skewness of the distribution of such datasets. To overcome this problem, this study proposes a weighted decision tree algorithm that aims to remove the bias toward the majority class and prevents the reduction of majority observations in imbalance datasets classification. The proposed weighted decision tree algorithm was tested on three imbalanced datasets- cancer dataset, german credit dataset, and banknote dataset. The specificity, sensitivity, and accuracy metrics were used to evaluate the performance of the proposed decision tree algorithm on the datasets. The evaluation results show that for some of the weights of our proposed decision tree, the specificity, sensitivity, and accuracy metrics gave better results compared to that of the ID3 decision tree and decision tree induced with minority entropy for all three datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title=" decision tree"> decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=imbalance%20dataset" title=" imbalance dataset"> imbalance dataset</a> </p> <a href="https://publications.waset.org/abstracts/157609/a-ratio-weighted-decision-tree-algorithm-for-imbalance-dataset-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157609.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">136</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4671</span> Complex Decision Rules in the Form of Decision Trees</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Avinash%20S.%20Jagtap">Avinash S. Jagtap</a>, <a href="https://publications.waset.org/abstracts/search?q=Sharad%20D.%20Gore"> Sharad D. Gore</a>, <a href="https://publications.waset.org/abstracts/search?q=Rajendra%20G.%20Gurao"> Rajendra G. Gurao </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Decision rules become more and more complex as the number of conditions increase. As a consequence, the complexity of the decision rule also influences the time complexity of computer implementation of such a rule. Consider, for example, a decision that depends on four conditions A, B, C and D. For simplicity, suppose each of these four conditions is binary. Even then the decision rule will consist of 16 lines, where each line will be of the form: If A and B and C and D, then action 1. If A and B and C but not D, then action 2 and so on. While executing this decision rule, each of the four conditions will be checked every time until all the four conditions in a line are satisfied. The minimum number of logical comparisons is 4 whereas the maximum number is 64. This paper proposes to present a complex decision rule in the form of a decision tree. A decision tree divides the cases into branches every time a condition is checked. In the form of a decision tree, every branching eliminates half of the cases that do not satisfy the related conditions. As a result, every branch of the decision tree involves only four logical comparisons and hence is significantly simpler than the corresponding complex decision rule. The conclusion of this paper is that every complex decision rule can be represented as a decision tree and the decision tree is mathematically equivalent but computationally much simpler than the original complex decision rule <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=strategic" title="strategic">strategic</a>, <a href="https://publications.waset.org/abstracts/search?q=tactical" title=" tactical"> tactical</a>, <a href="https://publications.waset.org/abstracts/search?q=operational" title=" operational"> operational</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptive" title=" adaptive"> adaptive</a>, <a href="https://publications.waset.org/abstracts/search?q=innovative" title=" innovative"> innovative</a> </p> <a href="https://publications.waset.org/abstracts/77189/complex-decision-rules-in-the-form-of-decision-trees" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77189.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">286</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4670</span> A Novel PSO Based Decision Tree Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Farzan">Ali Farzan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Classification of data objects or patterns is a major part in most of Decision making systems. One of the popular and commonly used classification methods is Decision Tree (DT). It is a hierarchical decision making system by which a binary tree is constructed and starting from root, at each node some of the classes is rejected until reaching the leaf nods. Each leaf node is a representative of one specific class. Finding the splitting criteria in each node for constructing or training the tree is a major problem. Particle Swarm Optimization (PSO) has been adopted as a metaheuristic searching method for finding the best splitting criteria. Result of evaluating the proposed method over benchmark datasets indicates the higher accuracy of the new PSO based decision tree. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title="decision tree">decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=splitting%20criteria" title=" splitting criteria"> splitting criteria</a>, <a href="https://publications.waset.org/abstracts/search?q=metaheuristic" title=" metaheuristic"> metaheuristic</a> </p> <a href="https://publications.waset.org/abstracts/32425/a-novel-pso-based-decision-tree-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32425.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">406</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4669</span> Decision Tree Based Scheduling for Flexible Job Shops with Multiple Process Plans</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.-H.%20Doh">H.-H. Doh</a>, <a href="https://publications.waset.org/abstracts/search?q=J.-M.%20Yu"> J.-M. Yu</a>, <a href="https://publications.waset.org/abstracts/search?q=Y.-J.%20Kwon"> Y.-J. Kwon</a>, <a href="https://publications.waset.org/abstracts/search?q=J.-H.%20Shin"> J.-H. Shin</a>, <a href="https://publications.waset.org/abstracts/search?q=H.-W.%20Kim"> H.-W. Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=S.-H.%20Nam"> S.-H. Nam</a>, <a href="https://publications.waset.org/abstracts/search?q=D.-H.%20Lee"> D.-H. Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper suggests a decision tree based approach for flexible job shop scheduling with multiple process plans, i. e. each job can be processed through alternative operations, each of which can be processed on alternative machines. The main decision variables are: (a) selecting operation/machine pair; and (b) sequencing the jobs assigned to each machine. As an extension of the priority scheduling approach that selects the best priority rule combination after many simulation runs, this study suggests a decision tree based approach in which a decision tree is used to select a priority rule combination adequate for a specific system state and hence the burdens required for developing simulation models and carrying out simulation runs can be eliminated. The decision tree based scheduling approach consists of construction and scheduling modules. In the construction module, a decision tree is constructed using a four-stage algorithm, and in the scheduling module, a priority rule combination is selected using the decision tree. To show the performance of the decision tree based approach suggested in this study, a case study was done on a flexible job shop with reconfigurable manufacturing cells and a conventional job shop, and the results are reported by comparing it with individual priority rule combinations for the objectives of minimizing total flow time and total tardiness. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=flexible%20job%20shop%20scheduling" title="flexible job shop scheduling">flexible job shop scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title=" decision tree"> decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=priority%20rules" title=" priority rules"> priority rules</a>, <a href="https://publications.waset.org/abstracts/search?q=case%20study" title=" case study"> case study</a> </p> <a href="https://publications.waset.org/abstracts/6996/decision-tree-based-scheduling-for-flexible-job-shops-with-multiple-process-plans" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6996.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">357</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4668</span> Model for Introducing Products to New Customers through Decision Tree Using Algorithm C4.5 (J-48)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Komol%20Phaisarn">Komol Phaisarn</a>, <a href="https://publications.waset.org/abstracts/search?q=Anuphan%20Suttimarn"> Anuphan Suttimarn</a>, <a href="https://publications.waset.org/abstracts/search?q=Vitchanan%20Keawtong"> Vitchanan Keawtong</a>, <a href="https://publications.waset.org/abstracts/search?q=Kittisak%20Thongyoun"> Kittisak Thongyoun</a>, <a href="https://publications.waset.org/abstracts/search?q=Chaiyos%20Jamsawang"> Chaiyos Jamsawang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This article is intended to analyze insurance information which contains information on the customer decision when purchasing life insurance pay package. The data were analyzed in order to present new customers with Life Insurance Perfect Pay package to meet new customers’ needs as much as possible. The basic data of insurance pay package were collect to get data mining; thus, reducing the scattering of information. The data were then classified in order to get decision model or decision tree using Algorithm C4.5 (J-48). In the classification, WEKA tools are used to form the model and testing datasets are used to test the decision tree for the accurate decision. The validation of this model in classifying showed that the accurate prediction was 68.43% while 31.25% were errors. The same set of data were then tested with other models, i.e. Naive Bayes and Zero R. The results showed that J-48 method could predict more accurately. So, the researcher applied the decision tree in writing the program used to introduce the product to new customers to persuade customers’ decision making in purchasing the insurance package that meets the new customers’ needs as much as possible. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title="decision tree">decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=customers" title=" customers"> customers</a>, <a href="https://publications.waset.org/abstracts/search?q=life%20insurance%20pay%20package" title=" life insurance pay package"> life insurance pay package</a> </p> <a href="https://publications.waset.org/abstracts/11724/model-for-introducing-products-to-new-customers-through-decision-tree-using-algorithm-c45-j-48" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11724.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">427</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4667</span> Artificial Neural Networks with Decision Trees for Diagnosis Issues</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Y.%20Kourd">Y. Kourd</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Lefebvre"> D. Lefebvre</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Guersi"> N. Guersi </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a new idea for fault detection and isolation (FDI) technique which is applied to industrial system. This technique is based on Neural Networks fault-free and Faulty behaviors Models (NNFM's). NNFM's are used for residual generation, while decision tree architecture is used for residual evaluation. The decision tree is realized with data collected from the NNFM’s outputs and is used to isolate detectable faults depending on computed threshold. Each part of the tree corresponds to specific residual. With the decision tree, it becomes possible to take the appropriate decision regarding the actual process behavior by evaluating few numbers of residuals. In comparison to usual systematic evaluation of all residuals, the proposed technique requires less computational effort and can be used for on line diagnosis. An application example is presented to illustrate and confirm the effectiveness and the accuracy of the proposed approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title="neural networks">neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20trees" title=" decision trees"> decision trees</a>, <a href="https://publications.waset.org/abstracts/search?q=diagnosis" title=" diagnosis"> diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=behaviors" title=" behaviors"> behaviors</a> </p> <a href="https://publications.waset.org/abstracts/8203/artificial-neural-networks-with-decision-trees-for-diagnosis-issues" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8203.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">505</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4666</span> A Comparison of Single of Decision Tree, Decision Tree Forest and Group Method of Data Handling to Evaluate the Surface Roughness in Machining Process </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Ghorbani">S. Ghorbani</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20I.%20Polushin"> N. I. Polushin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The machinability of workpieces (AISI 1045 Steel, AA2024 aluminum alloy, A48-class30 gray cast iron) in turning operation has been carried out using different types of cutting tool (conventional, cutting tool with holes in toolholder and cutting tool filled up with composite material) under dry conditions on a turning machine at different stages of spindle speed (630-1000 rpm), feed rate (0.05-0.075 mm/rev), depth of cut (0.05-0.15 mm) and tool overhang (41-65 mm). Experimentation was performed as per Taguchi’s orthogonal array. To evaluate the relative importance of factors affecting surface roughness the single decision tree (SDT), Decision tree forest (DTF) and Group method of data handling (GMDH) were applied. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=decision%20tree%20forest" title="decision tree forest">decision tree forest</a>, <a href="https://publications.waset.org/abstracts/search?q=GMDH" title=" GMDH"> GMDH</a>, <a href="https://publications.waset.org/abstracts/search?q=surface%20roughness" title=" surface roughness"> surface roughness</a>, <a href="https://publications.waset.org/abstracts/search?q=Taguchi%20method" title=" Taguchi method"> Taguchi method</a>, <a href="https://publications.waset.org/abstracts/search?q=turning%20process" title=" turning process"> turning process</a> </p> <a href="https://publications.waset.org/abstracts/66804/a-comparison-of-single-of-decision-tree-decision-tree-forest-and-group-method-of-data-handling-to-evaluate-the-surface-roughness-in-machining-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/66804.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">441</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4665</span> Using Risk Management Indicators in Decision Tree Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adel%20Ali%20Elshaibani">Adel Ali Elshaibani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Risk management indicators augment the reporting infrastructure, particularly for the board and senior management, to identify, monitor, and manage risks. This enhancement facilitates improved decision-making throughout the banking organization. Decision tree analysis is a tool that visually outlines potential outcomes, costs, and consequences of complex decisions. It is particularly beneficial for analyzing quantitative data and making decisions based on numerical values. By calculating the expected value of each outcome, decision tree analysis can help assess the best course of action. In the context of banking, decision tree analysis can assist lenders in evaluating a customer’s creditworthiness, thereby preventing losses. However, applying these tools in developing countries may face several limitations, such as data availability, lack of technological infrastructure and resources, lack of skilled professionals, cultural factors, and cost. Moreover, decision trees can create overly complex models that do not generalize well to new data, known as overfitting. They can also be sensitive to small changes in the data, which can result in different tree structures and can become computationally expensive when dealing with large datasets. In conclusion, while risk management indicators and decision tree analysis are beneficial for decision-making in banks, their effectiveness is contingent upon how they are implemented and utilized by the board of directors, especially in the context of developing countries. It’s important to consider these limitations when planning to implement these tools in developing countries. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=risk%20management%20indicators" title="risk management indicators">risk management indicators</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree%20analysis" title=" decision tree analysis"> decision tree analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=developing%20countries" title=" developing countries"> developing countries</a>, <a href="https://publications.waset.org/abstracts/search?q=board%20of%20directors" title=" board of directors"> board of directors</a>, <a href="https://publications.waset.org/abstracts/search?q=bank%20performance" title=" bank performance"> bank performance</a>, <a href="https://publications.waset.org/abstracts/search?q=risk%20management%20strategy" title=" risk management strategy"> risk management strategy</a>, <a href="https://publications.waset.org/abstracts/search?q=banking%20institutions" title=" banking institutions"> banking institutions</a> </p> <a href="https://publications.waset.org/abstracts/176212/using-risk-management-indicators-in-decision-tree-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/176212.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">60</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4664</span> An Alternative Approach for Assessing the Impact of Cutting Conditions on Surface Roughness Using Single Decision Tree</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Ghorbani">S. Ghorbani</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20I.%20Polushin"> N. I. Polushin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, an approach to identify factors affecting on surface roughness in a machining process is presented. This study is based on 81 data about surface roughness over a wide range of cutting tools (conventional, cutting tool with holes, cutting tool with composite material), workpiece materials (AISI 1045 Steel, AA2024 aluminum alloy, A48-class30 gray cast iron), spindle speed (630-1000 rpm), feed rate (0.05-0.075 mm/rev), depth of cut (0.05-0.15 mm) and tool overhang (41-65 mm). A single decision tree (SDT) analysis was done to identify factors for predicting a model of surface roughness, and the CART algorithm was employed for building and evaluating regression tree. Results show that a single decision tree is better than traditional regression models with higher rate and forecast accuracy and strong value. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cutting%20condition" title="cutting condition">cutting condition</a>, <a href="https://publications.waset.org/abstracts/search?q=surface%20roughness" title=" surface roughness"> surface roughness</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title=" decision tree"> decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=CART%20algorithm" title=" CART algorithm"> CART algorithm</a> </p> <a href="https://publications.waset.org/abstracts/70715/an-alternative-approach-for-assessing-the-impact-of-cutting-conditions-on-surface-roughness-using-single-decision-tree" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/70715.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">375</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4663</span> Real-Time Classification of Marbles with Decision-Tree Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20S.%20Parlak">K. S. Parlak</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20Turan"> E. Turan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The separation of marbles according to the pattern quality is a process made according to expert decision. The classification phase is the most critical part in terms of economic value. In this study, a self-learning system is proposed which performs the classification of marbles quickly and with high success. This system performs ten feature extraction by taking ten marble images from the camera. The marbles are classified by decision tree method using the obtained properties. The user forms the training set by training the system at the marble classification stage. The system evolves itself in every marble image that is classified. The aim of the proposed system is to minimize the error caused by the person performing the classification and achieve it quickly. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title="decision tree">decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=k-means%20clustering" title=" k-means clustering"> k-means clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=marble%20classification" title=" marble classification"> marble classification</a> </p> <a href="https://publications.waset.org/abstracts/76038/real-time-classification-of-marbles-with-decision-tree-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/76038.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">382</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4662</span> Understanding Farmers’ Perceptions Towards Agrivoltaics Using Decision Tree Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mayuri%20Roy%20Choudhury">Mayuri Roy Choudhury</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent times the concept of agrivoltaics has gained popularity due to the dual use of land and the added value provided by photovoltaics in terms of renewable energy and crop production on farms. However, the transition towards agrivoltaics has been slow, and our research tries to investigate the obstacles leading towards the slow progress of agrivoltaics. We applied data science decision tree algorithms to quantify qualitative perceptions of farmers in the United States for agrivoltaics. To date, there has not been much research that mentions farmers' perceptions, as most of the research focuses on the benefits of agrivoltaics. Our study adds value by putting forward the voices of farmers, which play a crucial towards the transition to agrivoltaics in the future. Our results show a mixture of responses in favor of agrivoltaics. Furthermore, it also portrays significant concerns of farmers, which is useful for decision-makers when it comes to formulating policies for agrivoltaics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=agrivoltaics" title="agrivoltaics">agrivoltaics</a>, <a href="https://publications.waset.org/abstracts/search?q=decision-tree%20algorithms" title=" decision-tree algorithms"> decision-tree algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=farmers%20perception" title=" farmers perception"> farmers perception</a>, <a href="https://publications.waset.org/abstracts/search?q=transition" title=" transition"> transition</a> </p> <a href="https://publications.waset.org/abstracts/139772/understanding-farmers-perceptions-towards-agrivoltaics-using-decision-tree-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/139772.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">190</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4661</span> Survey on Big Data Stream Classification by Decision Tree</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mansoureh%20Ghiasabadi%20Farahani">Mansoureh Ghiasabadi Farahani</a>, <a href="https://publications.waset.org/abstracts/search?q=Samira%20Kalantary"> Samira Kalantary</a>, <a href="https://publications.waset.org/abstracts/search?q=Sara%20Taghi-Pour"> Sara Taghi-Pour</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahboubeh%20Shamsi"> Mahboubeh Shamsi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, the development of computers technology and its recent applications provide access to new types of data, which have not been considered by the traditional data analysts. Two particularly interesting characteristics of such data sets include their huge size and streaming nature .Incremental learning techniques have been used extensively to address the data stream classification problem. This paper presents a concise survey on the obstacles and the requirements issues classifying data streams with using decision tree. The most important issue is to maintain a balance between accuracy and efficiency, the algorithm should provide good classification performance with a reasonable time response. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20streams" title=" data streams"> data streams</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title=" decision tree"> decision tree</a> </p> <a href="https://publications.waset.org/abstracts/31107/survey-on-big-data-stream-classification-by-decision-tree" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31107.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">521</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4660</span> Decision Tree Modeling in Emergency Logistics Planning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yousef%20Abu%20Nahleh">Yousef Abu Nahleh</a>, <a href="https://publications.waset.org/abstracts/search?q=Arun%20Kumar"> Arun Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Fugen%20Daver"> Fugen Daver</a>, <a href="https://publications.waset.org/abstracts/search?q=Reham%20Al-Hindawi"> Reham Al-Hindawi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Despite the availability of natural disaster related time series data for last 110 years, there is no forecasting tool available to humanitarian relief organizations to determine forecasts for emergency logistics planning. This study develops a forecasting tool based on identifying probability of disaster for each country in the world by using decision tree modeling. Further, the determination of aggregate forecasts leads to efficient pre-disaster planning. Based on the research findings, the relief agencies can optimize the various resources allocation in emergency logistics planning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=decision%20tree%20modeling" title="decision tree modeling">decision tree modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting" title=" forecasting"> forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=humanitarian%20relief" title=" humanitarian relief"> humanitarian relief</a>, <a href="https://publications.waset.org/abstracts/search?q=emergency%20supply%20chain" title=" emergency supply chain"> emergency supply chain</a> </p> <a href="https://publications.waset.org/abstracts/7989/decision-tree-modeling-in-emergency-logistics-planning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7989.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">483</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4659</span> Performance Analysis of Artificial Neural Network with Decision Tree in Prediction of Diabetes Mellitus</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=J.%20K.%20Alhassan">J. K. Alhassan</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Attah"> B. Attah</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Misra"> S. Misra</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Human beings have the ability to make logical decisions. Although human decision - making is often optimal, it is insufficient when huge amount of data is to be classified. medical dataset is a vital ingredient used in predicting patients health condition. In other to have the best prediction, there calls for most suitable machine learning algorithms. This work compared the performance of Artificial Neural Network (ANN) and Decision Tree Algorithms (DTA) as regards to some performance metrics using diabetes data. The evaluations was done using weka software and found out that DTA performed better than ANN. Multilayer Perceptron (MLP) and Radial Basis Function (RBF) were the two algorithms used for ANN, while RegTree and LADTree algorithms were the DTA models used. The Root Mean Squared Error (RMSE) of MLP is 0.3913,that of RBF is 0.3625, that of RepTree is 0.3174 and that of LADTree is 0.3206 respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title="artificial neural network">artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree%20algorithms" title=" decision tree algorithms"> decision tree algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=diabetes%20mellitus" title=" diabetes mellitus"> diabetes mellitus</a> </p> <a href="https://publications.waset.org/abstracts/35949/performance-analysis-of-artificial-neural-network-with-decision-tree-in-prediction-of-diabetes-mellitus" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35949.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">408</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4658</span> A Case-Based Reasoning-Decision Tree Hybrid System for Stock Selection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yaojun%20Wang">Yaojun Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Yaoqing%20Wang"> Yaoqing Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Stock selection is an important decision-making problem. Many machine learning and data mining technologies are employed to build automatic stock-selection system. A profitable stock-selection system should consider the stock’s investment value and the market timing. In this paper, we present a hybrid system including both engage for stock selection. This system uses a case-based reasoning (CBR) model to execute the stock classification, uses a decision-tree model to help with market timing and stock selection. The experiments show that the performance of this hybrid system is better than that of other techniques regarding to the classification accuracy, the average return and the Sharpe ratio. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=case-based%20reasoning" title="case-based reasoning">case-based reasoning</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title=" decision tree"> decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=stock%20selection" title=" stock selection"> stock selection</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/48974/a-case-based-reasoning-decision-tree-hybrid-system-for-stock-selection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48974.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">419</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4657</span> Faults Diagnosis by Thresholding and Decision tree with Neuro-Fuzzy System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Y.%20Kourd">Y. Kourd</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Lefebvre"> D. Lefebvre</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The monitoring of industrial processes is required to ensure operating conditions of industrial systems through automatic detection and isolation of faults. This paper proposes a method of fault diagnosis based on a neuro-fuzzy hybrid structure. This hybrid structure combines the selection of threshold and decision tree. The validation of this method is obtained with the DAMADICS benchmark. In the first phase of the method, a model will be constructed that represents the normal state of the system to fault detection. Signatures of the faults are obtained with residuals analysis and selection of appropriate thresholds. These signatures provide groups of non-separable faults. In the second phase, we build faulty models to see the flaws in the system that cannot be isolated in the first phase. In the latest phase we construct the tree that isolates these faults. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title="decision tree">decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=residuals%20analysis" title=" residuals analysis"> residuals analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=ANFIS" title=" ANFIS"> ANFIS</a>, <a href="https://publications.waset.org/abstracts/search?q=fault%20diagnosis" title=" fault diagnosis"> fault diagnosis</a> </p> <a href="https://publications.waset.org/abstracts/26932/faults-diagnosis-by-thresholding-and-decision-tree-with-neuro-fuzzy-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26932.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">625</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4656</span> Amharic Text News Classification Using Supervised Learning </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Misrak%20Assefa">Misrak Assefa </a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Amharic language is the second most widely spoken Semitic language in the world. There are several new overloaded on the web. Searching some useful documents from the web on a specific topic, which is written in the Amharic language, is a challenging task. Hence, document categorization is required for managing and filtering important information. In the classification of Amharic text news, there is still a gap in the domain of information that needs to be launch. This study attempts to design an automatic Amharic news classification using a supervised learning mechanism on four un-touch classes. To achieve this research, 4,182 news articles were used. Naive Bayes (NB) and Decision tree (j48) algorithms were used to classify the given Amharic dataset. In this paper, k-fold cross-validation is used to estimate the accuracy of the classifier. As a result, it shows those algorithms can be applicable in Amharic news categorization. The best average accuracy result is achieved by j48 decision tree and naïve Bayes is 95.2345 %, and 94.6245 % respectively using three categories. This research indicated that a typical decision tree algorithm is more applicable to Amharic news categorization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=text%20categorization" title="text categorization">text categorization</a>, <a href="https://publications.waset.org/abstracts/search?q=supervised%20machine%20learning" title=" supervised machine learning"> supervised machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=naive%20Bayes" title=" naive Bayes"> naive Bayes</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title=" decision tree"> decision tree</a> </p> <a href="https://publications.waset.org/abstracts/124249/amharic-text-news-classification-using-supervised-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/124249.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">209</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4655</span> Using Data Mining Technique for Scholarship Disbursement</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=J.%20K.%20Alhassan">J. K. Alhassan</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20A.%20Lawal"> S. A. Lawal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work is on decision tree-based classification for the disbursement of scholarship. Tree-based data mining classification technique is used in other to determine the generic rule to be used to disburse the scholarship. The system based on the defined rules from the tree is able to determine the class (status) to which an applicant shall belong whether Granted or Not Granted. The applicants that fall to the class of granted denote a successful acquirement of scholarship while those in not granted class are unsuccessful in the scheme. An algorithm that can be used to classify the applicants based on the rules from tree-based classification was also developed. The tree-based classification is adopted because of its efficiency, effectiveness, and easy to comprehend features. The system was tested with the data of National Information Technology Development Agency (NITDA) Abuja, a Parastatal of Federal Ministry of Communication Technology that is mandated to develop and regulate information technology in Nigeria. The system was found working according to the specification. It is therefore recommended for all scholarship disbursement organizations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classification" title="classification">classification</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title=" decision tree"> decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=scholarship" title=" scholarship"> scholarship</a> </p> <a href="https://publications.waset.org/abstracts/30987/using-data-mining-technique-for-scholarship-disbursement" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30987.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">375</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4654</span> Commuters Trip Purpose Decision Tree Based Model of Makurdi Metropolis, Nigeria and Strategic Digital City Project</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Emmanuel%20Okechukwu%20Nwafor">Emmanuel Okechukwu Nwafor</a>, <a href="https://publications.waset.org/abstracts/search?q=Folake%20Olubunmi%20Akintayo"> Folake Olubunmi Akintayo</a>, <a href="https://publications.waset.org/abstracts/search?q=Denis%20Alcides%20Rezende"> Denis Alcides Rezende</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Decision tree models are versatile and interpretable machine learning algorithms widely used for both classification and regression tasks, which can be related to cities, whether physical or digital. The aim of this research is to assess how well decision tree algorithms can predict trip purposes in Makurdi, Nigeria, while also exploring their connection to the strategic digital city initiative. The research methodology involves formalizing household demographic and trips information datasets obtained from extensive survey process. Modelling and Prediction were achieved using Python Programming Language and the evaluation metrics like R-squared and mean absolute error were used to assess the decision tree algorithm's performance. The results indicate that the model performed well, with accuracies of 84% and 68%, and low MAE values of 0.188 and 0.314, on training and validation data, respectively. This suggests the model can be relied upon for future prediction. The conclusion reiterates that This model will assist decision-makers, including urban planners, transportation engineers, government officials, and commuters, in making informed decisions on transportation planning and management within the framework of a strategic digital city. Its application will enhance the efficiency, sustainability, and overall quality of transportation services in Makurdi, Nigeria. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=decision%20tree%20algorithm" title="decision tree algorithm">decision tree algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=trip%20purpose" title=" trip purpose"> trip purpose</a>, <a href="https://publications.waset.org/abstracts/search?q=intelligent%20transport" title=" intelligent transport"> intelligent transport</a>, <a href="https://publications.waset.org/abstracts/search?q=strategic%20digital%20city" title=" strategic digital city"> strategic digital city</a>, <a href="https://publications.waset.org/abstracts/search?q=travel%20pattern" title=" travel pattern"> travel pattern</a>, <a href="https://publications.waset.org/abstracts/search?q=sustainable%20transport" title=" sustainable transport"> sustainable transport</a> </p> <a href="https://publications.waset.org/abstracts/191019/commuters-trip-purpose-decision-tree-based-model-of-makurdi-metropolis-nigeria-and-strategic-digital-city-project" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/191019.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">20</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4653</span> Decision Tree Analysis of Risk Factors for Intravenous Infiltration among Hospitalized Children: A Retrospective Study </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Soon-Mi%20Park">Soon-Mi Park</a>, <a href="https://publications.waset.org/abstracts/search?q=Ihn%20Sook%20Jeong"> Ihn Sook Jeong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This retrospective study was aimed to identify risk factors of intravenous (IV) infiltration for hospitalized children. The participants were 1,174 children for test and 424 children for validation, who admitted to a general hospital, received peripheral intravenous injection therapy at least once and had complete records. Data were analyzed with frequency and percentage or mean and standard deviation were calculated, and decision tree analysis was used to screen for the most important risk factors for IV infiltration for hospitalized children. The decision tree analysis showed that the most important traditional risk factors for IV infiltration were the use of ampicillin/sulbactam, IV insertion site (lower extremities), and medical department (internal medicine) both in the test sample and validation sample. The correct classification was 92.2% in the test sample and 90.1% in the validation sample. More careful attention should be made to patients who are administered ampicillin/sulbactam, have IV site in lower extremities and have internal medical problems to prevent or detect infiltration occurrence. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=decision%20tree%20analysis" title="decision tree analysis">decision tree analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=intravenous%20infiltration" title=" intravenous infiltration"> intravenous infiltration</a>, <a href="https://publications.waset.org/abstracts/search?q=child" title=" child"> child</a>, <a href="https://publications.waset.org/abstracts/search?q=validation" title=" validation"> validation</a> </p> <a href="https://publications.waset.org/abstracts/96898/decision-tree-analysis-of-risk-factors-for-intravenous-infiltration-among-hospitalized-children-a-retrospective-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/96898.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">176</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4652</span> Using Single Decision Tree to Assess the Impact of Cutting Conditions on Vibration</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Ghorbani">S. Ghorbani</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20I.%20Polushin"> N. I. Polushin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Vibration during machining process is crucial since it affects cutting tool, machine, and workpiece leading to a tool wear, tool breakage, and an unacceptable surface roughness. This paper applies a nonparametric statistical method, single decision tree (SDT), to identify factors affecting on vibration in machining process. Workpiece material (AISI 1045 Steel, AA2024 Aluminum alloy, A48-class30 Gray Cast Iron), cutting tool (conventional, cutting tool with holes in toolholder, cutting tool filled up with epoxy-granite), tool overhang (41-65 mm), spindle speed (630-1000 rpm), feed rate (0.05-0.075 mm/rev) and depth of cut (0.05-0.15 mm) were used as input variables, while vibration was the output parameter. It is concluded that workpiece material is the most important parameters for natural frequency followed by cutting tool and overhang. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cutting%20condition" title="cutting condition">cutting condition</a>, <a href="https://publications.waset.org/abstracts/search?q=vibration" title=" vibration"> vibration</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20frequency" title=" natural frequency"> natural frequency</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title=" decision tree"> decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=CART%20algorithm" title=" CART algorithm"> CART algorithm</a> </p> <a href="https://publications.waset.org/abstracts/52496/using-single-decision-tree-to-assess-the-impact-of-cutting-conditions-on-vibration" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52496.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">336</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4651</span> Empirical and Indian Automotive Equity Portfolio Decision Support</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20Sankar">P. Sankar</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20James%20Daniel%20Paul"> P. James Daniel Paul</a>, <a href="https://publications.waset.org/abstracts/search?q=Siddhant%20Sahu"> Siddhant Sahu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A brief review of the empirical studies on the methodology of the stock market decision support would indicate that they are at a threshold of validating the accuracy of the traditional and the fuzzy, artificial neural network and the decision trees. Many researchers have been attempting to compare these models using various data sets worldwide. However, the research community is on the way to the conclusive confidence in the emerged models. This paper attempts to use the automotive sector stock prices from National Stock Exchange (NSE), India and analyze them for the intra-sectorial support for stock market decisions. The study identifies the significant variables and their lags which affect the price of the stocks using OLS analysis and decision tree classifiers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Indian%20automotive%20sector" title="Indian automotive sector">Indian automotive sector</a>, <a href="https://publications.waset.org/abstracts/search?q=stock%20market%20decisions" title=" stock market decisions"> stock market decisions</a>, <a href="https://publications.waset.org/abstracts/search?q=equity%20portfolio%20analysis" title=" equity portfolio analysis"> equity portfolio analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree%20classifiers" title=" decision tree classifiers"> decision tree classifiers</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20data%20analysis" title=" statistical data analysis"> statistical data analysis</a> </p> <a href="https://publications.waset.org/abstracts/6454/empirical-and-indian-automotive-equity-portfolio-decision-support" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6454.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">485</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4650</span> Decision Tree Model for the Recommendation of Digital and Alternate Payment Methods for SMEs</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arturo%20J.%20Anci%20Alm%C3%A9star">Arturo J. Anci Alméstar</a>, <a href="https://publications.waset.org/abstracts/search?q=Jose%20D.%20Fernandez%20Huapaya"> Jose D. Fernandez Huapaya</a>, <a href="https://publications.waset.org/abstracts/search?q=David%20Mauricio"> David Mauricio</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Companies make erroneous decisions by not evaluating the inherent difficulties of entering electronic commerce without a prior review of current digital and alternate means of payment. For this reason, it is very important for businesses to have reliable, complete and integrated information on the means of current digital and alternate payments that allow decisions to be made about which of these to use. However, there is no such consolidated information or criteria that companies use to make decisions about the means of payment according to their needs. In this paper, we propose a decision tree model based on a taxonomy that presents us with a categorization of digital and alternative means of payment, as well as the visualization of the flow of information at a high level from the company to obtain a recommendation. This will allow the company to make the most appropriate decision about the implementation of the digital means of payment or alternative ideal for their needs, which allows a reduction in costs and complexity of the payment process. Likewise, the efficiency of the proposed model was evaluated through a satisfaction survey presented to company personnel, confirming the satisfactory quality level of the recommendations obtained by the model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=digital%20payment%20medium" title="digital payment medium">digital payment medium</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title=" decision tree"> decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20making" title=" decision making"> decision making</a>, <a href="https://publications.waset.org/abstracts/search?q=digital%20payments%20taxonomy" title=" digital payments taxonomy"> digital payments taxonomy</a> </p> <a href="https://publications.waset.org/abstracts/85328/decision-tree-model-for-the-recommendation-of-digital-and-alternate-payment-methods-for-smes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/85328.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">179</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4649</span> Historical Landscape Affects Present Tree Density in Paddy Field</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ha%20T.%20Pham">Ha T. Pham</a>, <a href="https://publications.waset.org/abstracts/search?q=Shuichi%20Miyagawa"> Shuichi Miyagawa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ongoing landscape transformation is one of the major causes behind disappearance of traditional landscapes, and lead to species and resource loss. Tree in paddy fields in the northeast of Thailand is one of those traditional landscapes. Using three different historical time layers, we acknowledged the severe deforestation and rapid urbanization happened in the region. Despite the general thinking of decline in tree density as consequences, the heterogeneous trend of changes in total tree density in three studied landscapes denied the hypothesis that number of trees in paddy field depend on the length of land use practice. On the other hand, due to selection of planting new trees on levees, existence of trees in paddy field are now rely on their values for human use. Besides, changes in land use and landscape structure had a significant impact on decision of which tree density level is considered as suitable for the landscape. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aerial%20photographs" title="aerial photographs">aerial photographs</a>, <a href="https://publications.waset.org/abstracts/search?q=land%20use%20change" title=" land use change"> land use change</a>, <a href="https://publications.waset.org/abstracts/search?q=traditional%20landscape" title=" traditional landscape"> traditional landscape</a>, <a href="https://publications.waset.org/abstracts/search?q=tree%20in%20paddy%20fields" title=" tree in paddy fields"> tree in paddy fields</a> </p> <a href="https://publications.waset.org/abstracts/15536/historical-landscape-affects-present-tree-density-in-paddy-field" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15536.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">419</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4648</span> Machine Learning Predictive Models for Hydroponic Systems: A Case Study Nutrient Film Technique and Deep Flow Technique</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kritiyaporn%20Kunsook">Kritiyaporn Kunsook</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Machine learning algorithms (MLAs) such us artificial neural networks (ANNs), decision tree, support vector machines (SVMs), Naïve Bayes, and ensemble classifier by voting are powerful data driven methods that are relatively less widely used in the mapping of technique of system, and thus have not been comparatively evaluated together thoroughly in this field. The performances of a series of MLAs, ANNs, decision tree, SVMs, Naïve Bayes, and ensemble classifier by voting in technique of hydroponic systems prospectively modeling are compared based on the accuracy of each model. Classification of hydroponic systems only covers the test samples from vegetables grown with Nutrient film technique (NFT) and Deep flow technique (DFT). The feature, which are the characteristics of vegetables compose harvesting height width, temperature, require light and color. The results indicate that the classification performance of the ANNs is 98%, decision tree is 98%, SVMs is 97.33%, Naïve Bayes is 96.67%, and ensemble classifier by voting is 98.96% algorithm respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20networks" title="artificial neural networks">artificial neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title=" decision tree"> decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machines" title=" support vector machines"> support vector machines</a>, <a href="https://publications.waset.org/abstracts/search?q=na%C3%AFve%20Bayes" title=" naïve Bayes"> naïve Bayes</a>, <a href="https://publications.waset.org/abstracts/search?q=ensemble%20classifier%20by%20voting" title=" ensemble classifier by voting"> ensemble classifier by voting</a> </p> <a href="https://publications.waset.org/abstracts/91070/machine-learning-predictive-models-for-hydroponic-systems-a-case-study-nutrient-film-technique-and-deep-flow-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/91070.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">372</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4647</span> A New DIDS Design Based on a Combination Feature Selection Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adel%20Sabry%20Eesa">Adel Sabry Eesa</a>, <a href="https://publications.waset.org/abstracts/search?q=Adnan%20Mohsin%20Abdulazeez%20Brifcani"> Adnan Mohsin Abdulazeez Brifcani</a>, <a href="https://publications.waset.org/abstracts/search?q=Zeynep%20Orman"> Zeynep Orman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Feature selection has been used in many fields such as classification, data mining and object recognition and proven to be effective for removing irrelevant and redundant features from the original data set. In this paper, a new design of distributed intrusion detection system using a combination feature selection model based on bees and decision tree. Bees algorithm is used as the search strategy to find the optimal subset of features, whereas decision tree is used as a judgment for the selected features. Both the produced features and the generated rules are used by Decision Making Mobile Agent to decide whether there is an attack or not in the networks. Decision Making Mobile Agent will migrate through the networks, moving from node to another, if it found that there is an attack on one of the nodes, it then alerts the user through User Interface Agent or takes some action through Action Mobile Agent. The KDD Cup 99 data set is used to test the effectiveness of the proposed system. The results show that even if only four features are used, the proposed system gives a better performance when it is compared with the obtained results using all 41 features. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distributed%20intrusion%20detection%20system" title="distributed intrusion detection system">distributed intrusion detection system</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20agent" title=" mobile agent"> mobile agent</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title=" feature selection"> feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=bees%20algorithm" title=" bees algorithm"> bees algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title=" decision tree"> decision tree</a> </p> <a href="https://publications.waset.org/abstracts/32661/a-new-dids-design-based-on-a-combination-feature-selection-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32661.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">408</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4646</span> Corporate Governance and Disclosure Quality: Taxonomy of Tunisian Listed Firms Using the Decision Tree Method Based Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wided%20Khiari">Wided Khiari</a>, <a href="https://publications.waset.org/abstracts/search?q=Adel%20Karaa"> Adel Karaa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study aims to establish a typology of Tunisian listed firms according to their corporate governance characteristics and disclosure quality. The paper uses disclosed scores to examine corporate governance practices of Tunisian listed firms. A content analysis of 46 Tunisian listed firms from 2001 to 2010 has been carried out and a disclosure index developed to determine the level of disclosure of the companies. The disclosure quality is appreciated through the quantity and also through the nature (type) of information disclosed. Applying the decision tree method, the obtained tree diagrams provide ways to know the characteristics of a particular firm regardless of its level of disclosure. Obtained results show that the characteristics of corporate governance to achieve good quality of disclosure are not unique for all firms. These structures are not necessarily all of the recommendations of best practices, but converge towards the best combination. Indeed, in practice, there are companies which have a good quality of disclosure, but are not well-governed. However, we hope that by improving their governance system their level of disclosure may be better. These findings show, in a general way, a convergence towards the standards of corporate governance with a few exceptions related to the specificity of Tunisian listed firms and show the need for the adoption of a code for each context. These findings shed the light on corporate governance features that enhance incentives for good disclosure. It allows identifying, for each firm and in any date, corporate governance determinants of disclosure quality. More specifically, and all being equal, obtained tree makes a rule of decision for the company to know the level of disclosure based on certain characteristics of the governance strategy adopted by the latter. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=corporate%20governance" title="corporate governance">corporate governance</a>, <a href="https://publications.waset.org/abstracts/search?q=disclosure" title=" disclosure"> disclosure</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title=" decision tree"> decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=economics" title=" economics"> economics</a> </p> <a href="https://publications.waset.org/abstracts/5738/corporate-governance-and-disclosure-quality-taxonomy-of-tunisian-listed-firms-using-the-decision-tree-method-based-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5738.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">335</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4645</span> A Decision Tree Approach to Estimate Permanent Residents Using Remote Sensing Data in Lebanese Municipalities</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20Allaw">K. Allaw</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20Adjizian%20Gerard"> J. Adjizian Gerard</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Chehayeb"> M. Chehayeb</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Raad"> A. Raad</a>, <a href="https://publications.waset.org/abstracts/search?q=W.%20Fahs"> W. Fahs</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Badran"> A. Badran</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Fakherdin"> A. Fakherdin</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Madi"> H. Madi</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Badaro%20Saliba"> N. Badaro Saliba</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Population estimation using Geographic Information System (GIS) and remote sensing faces many obstacles such as the determination of permanent residents. A permanent resident is an individual who stays and works during all four seasons in his village. So, all those who move towards other cities or villages are excluded from this category. The aim of this study is to identify the factors affecting the percentage of permanent residents in a village and to determine the attributed weight to each factor. To do so, six factors have been chosen (slope, precipitation, temperature, number of services, time to Central Business District (CBD) and the proximity to conflict zones) and each one of those factors has been evaluated using one of the following data: the contour lines map of 50 m, the precipitation map, four temperature maps and data collected through surveys. The weighting procedure has been done using decision tree method. As a result of this procedure, temperature (50.8%) and percentage of precipitation (46.5%) are the most influencing factors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title="remote sensing">remote sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=GIS" title=" GIS"> GIS</a>, <a href="https://publications.waset.org/abstracts/search?q=permanent%20residence" title=" permanent residence"> permanent residence</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title=" decision tree"> decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=Lebanon" title=" Lebanon"> Lebanon</a> </p> <a href="https://publications.waset.org/abstracts/121948/a-decision-tree-approach-to-estimate-permanent-residents-using-remote-sensing-data-in-lebanese-municipalities" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/121948.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">133</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4644</span> Application of Machine Learning Techniques in Forest Cover-Type Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saba%20Ebrahimi">Saba Ebrahimi</a>, <a href="https://publications.waset.org/abstracts/search?q=Hedieh%20Ashrafi"> Hedieh Ashrafi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Predicting the cover type of forests is a challenge for natural resource managers. In this project, we aim to perform a comprehensive comparative study of two well-known classification methods, support vector machine (SVM) and decision tree (DT). The comparison is first performed among different types of each classifier, and then the best of each classifier will be compared by considering different evaluation metrics. The effect of boosting and bagging for decision trees is also explored. Furthermore, the effect of principal component analysis (PCA) and feature selection is also investigated. During the project, the forest cover-type dataset from the remote sensing and GIS program is used in all computations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classification%20methods" title="classification methods">classification methods</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title=" decision tree"> decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=forest%20cover-type%20dataset" title=" forest cover-type dataset"> forest cover-type dataset</a> </p> <a href="https://publications.waset.org/abstracts/137985/application-of-machine-learning-techniques-in-forest-cover-type-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/137985.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">217</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4643</span> Data Mining Algorithms Analysis: Case Study of Price Predictions of Lands</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Julio%20Albuja">Julio Albuja</a>, <a href="https://publications.waset.org/abstracts/search?q=David%20Zaldumbide"> David Zaldumbide</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data analysis is an important step before taking a decision about money. The aim of this work is to analyze the factors that influence the final price of the houses through data mining algorithms. To our best knowledge, previous work was researched just to compare results. Furthermore, before using the data of the data set, the Z-Transformation were used to standardize the data in the same range. Hence, the data was classified into two groups to visualize them in a readability format. A decision tree was built, and graphical data is displayed where clearly is easy to see the results and the factors' influence in these graphics. The definitions of these methods are described, as well as the descriptions of the results. Finally, conclusions and recommendations are presented related to the released results that our research showed making it easier to apply these algorithms using a customized data set. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=algorithms" title="algorithms">algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=data" title=" data"> data</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title=" decision tree"> decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=transformation" title=" transformation"> transformation</a> </p> <a href="https://publications.waset.org/abstracts/76382/data-mining-algorithms-analysis-case-study-of-price-predictions-of-lands" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/76382.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">374</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=decision%20tree&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=decision%20tree&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=decision%20tree&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=decision%20tree&page=5">5</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=decision%20tree&page=6">6</a></li> <li class="page-item"><a class="page-link" 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