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

Pateritsas, C; Stafylopatis, A

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
heal.bookName	LECTURE NOTES IN COMPUTER SCIENCE	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