A nearest features classifier using a self-organizing map for memory base evaluation

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dc.contributor.author Pateritsas, C en
dc.contributor.author Stafylopatis, A en
dc.date.accessioned 2014-03-01T02:43:50Z
dc.date.available 2014-03-01T02:43:50Z
dc.date.issued 2006 en
dc.identifier.issn 0302-9743 en
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/31525
dc.subject bayesian classifier en
dc.subject K Nearest Neighbor en
dc.subject Machine Learning en
dc.subject Memory Based Learning en
dc.subject Self Organized Map en
dc.subject.classification Computer Science, Theory & Methods en
dc.subject.other Bayesian classifier en
dc.subject.other Features classifier en
dc.subject.other K nearest neighbors en
dc.subject.other Memory base evaluation en
dc.subject.other Algorithms en
dc.subject.other Classification (of information) en
dc.subject.other Data storage equipment en
dc.subject.other Feature extraction en
dc.subject.other Learning systems en
dc.subject.other Mathematical models en
dc.subject.other Set theory en
dc.subject.other Self organizing maps en
dc.title A nearest features classifier using a self-organizing map for memory base evaluation en
heal.type conferenceItem en
heal.identifier.primary 10.1007/11840930_40 en
heal.identifier.secondary http://dx.doi.org/10.1007/11840930_40 en
heal.language English en
heal.publicationDate 2006 en
heal.abstract Memory base learning is one of main fields in the area of machine learning. We propose a new methodology for addressing the classification task that relies on the main idea of the k - nearest neighbors algorithm, which is the most important representative of this field. In the proposed approach, given an unclassified pattern, a set of neighboring patterns is found, but not necessarily using all input feature dimensions. Also, following the concept of the naïve Bayesian classifier, we adopt the hypothesis of the independence of input features in the outcome of the classification task. The two concepts are merged in an attempt to take advantage of their good performance features. In order to further improve the performance of our approach, we propose a novel weighting scheme of the memory base. Using the self-organizing maps model during the execution of the algorithm, dynamic weights of the memory base patterns are produced. Experimental results have shown superior performance of the proposed method in comparison with the aforementioned algorithms and their variations. © Springer-Verlag Berlin Heidelberg 2006. en
heal.publisher SPRINGER-VERLAG BERLIN en
heal.journalName Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) en
dc.identifier.doi 10.1007/11840930_40 en
dc.identifier.isi ISI:000241475200040 en
dc.identifier.volume 4132 LNCS - II en
dc.identifier.spage 391 en
dc.identifier.epage 400 en

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