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Towards a Balancing Medical Database by Using the Least Mean Square Algorithm
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<div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 87414</div> </div> </div> </div> <div class="card publication-listing mt-3 mb-3"> <h5 class="card-header" style="font-size:.9rem">Towards a Balancing Medical Database by Using the Least Mean Square Algorithm </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kamel%20Belammi">Kamel Belammi</a>, <a href="https://publications.waset.org/abstracts/search?q=Houria%20Fatrim"> Houria Fatrim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> imbalanced data set, a problem often found in real world application, can cause seriously negative effect on classification performance of machine learning algorithms. There have been many attempts at dealing with classification of imbalanced data sets. In medical diagnosis classification, we often face the imbalanced number of data samples between the classes in which there are not enough samples in rare classes. In this paper, we proposed a learning method based on a cost sensitive extension of Least Mean Square (LMS) algorithm that penalizes errors of different samples with different weight and some rules of thumb to determine those weights. After the balancing phase, we applythe different classifiers (support vector machine (SVM), k- nearest neighbor (KNN) and multilayer neuronal networks (MNN)) for balanced data set. We have also compared the obtained results before and after balancing method. <iframe src="https://publications.waset.org/abstracts/33277.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multilayer%20neural%20networks" title="multilayer neural networks">multilayer neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=k-%20nearest%20neighbor" title=" k- nearest neighbor"> k- nearest neighbor</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=imbalanced%20medical%20data" title=" imbalanced medical data"> imbalanced medical data</a>, <a href="https://publications.waset.org/abstracts/search?q=least%20mean%20square%20algorithm" title=" least mean square algorithm"> least mean square algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=diabetes" title=" diabetes"> diabetes</a> </p> <a href="https://publications.waset.org/abstracts/33277/towards-a-balancing-medical-database-by-using-the-least-mean-square-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33277.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">532</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" 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