Clonal selection-based neural classifier

Lanaridis, A; Karakasis, V; Stafylopatis, A

dc.contributor.author	Lanaridis, A	en
dc.contributor.author	Karakasis, V	en
dc.contributor.author	Stafylopatis, A	en
dc.date.accessioned	2014-03-01T02:45:11Z
dc.date.available	2014-03-01T02:45:11Z
dc.date.issued	2008	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32187
dc.subject	Artificial Immune System	en
dc.subject	Benchmark Problem	en
dc.subject	Clonal Selection	en
dc.subject	Multi Layer Perceptron	en
dc.subject.other	Artificial Immune Systems	en
dc.subject.other	Basic concepts	en
dc.subject.other	Benchmark problems	en
dc.subject.other	Clonal Selection Principles	en
dc.subject.other	Clonal selections	en
dc.subject.other	Input datums	en
dc.subject.other	Multilayer perceptrons	en
dc.subject.other	Multiple sets	en
dc.subject.other	Neural classifiers	en
dc.subject.other	Pattern recognition and classifications	en
dc.subject.other	Trained classifiers	en
dc.subject.other	Classifiers	en
dc.subject.other	Feature extraction	en
dc.subject.other	Intelligent control	en
dc.subject.other	Intelligent systems	en
dc.subject.other	Learning systems	en
dc.subject.other	Neural networks	en
dc.subject.other	Pattern recognition	en
dc.subject.other	Pattern recognition systems	en
dc.title	Clonal selection-based neural classifier	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/HIS.2008.82	en
heal.identifier.secondary	http://dx.doi.org/10.1109/HIS.2008.82	en
heal.identifier.secondary	4626705	en
heal.publicationDate	2008	en
heal.abstract	Artificial Immune Systems (AIS) constitute an emerging and promising field, and have been applied to pattern recognition and classification tasks to a limited extent so far. This work is a first attempt of applying the clonal selection principle to the training of MultiLayer Perceptrons (MLPs). The Clonal Selectionbased Neural Classifier (CSNC) uses the basic concepts of clonal selection to evolve MLPs, which are represented as real-valued linear antibodies. The proposed system is actually a multi-classifier, consisting of multiple sets of MLPs, each one devoted to the recognition of a different class of the input data. The final trained classifier is comprised of the best MLPs from each set. The proposed classifier is tested against a set of benchmark problems and yields promising results. © 2008 IEEE.	en
heal.journalName	Proceedings - 8th International Conference on Hybrid Intelligent Systems, HIS 2008	en
dc.identifier.doi	10.1109/HIS.2008.82	en
dc.identifier.spage	655	en
dc.identifier.epage	660	en