A divide-and-conquer method for multi-net classifiers

Frosyniotis, D; Stafylopatis, A; Likas, A

dc.contributor.author	Frosyniotis, D	en
dc.contributor.author	Stafylopatis, A	en
dc.contributor.author	Likas, A	en
dc.date.accessioned	2014-03-01T01:18:31Z
dc.date.available	2014-03-01T01:18:31Z
dc.date.issued	2003	en
dc.identifier.issn	1433-7541	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/15059
dc.subject	Classifier combination	en
dc.subject	Classifier fusion	en
dc.subject	Clustering	en
dc.subject	Divide-and-conquer	en
dc.subject	Multiple classifier systems	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.title	A divide-and-conquer method for multi-net classifiers	en
heal.type	journalArticle	en
heal.identifier.primary	10.1007/s10044-002-0174-6	en
heal.identifier.secondary	http://dx.doi.org/10.1007/s10044-002-0174-6	en
heal.language	English	en
heal.publicationDate	2003	en
heal.abstract	Several researchers have shown that substantial improvements can be achieved in difficult pattern recognition problems by combining the outputs of multiple neural networks. In this work, we present and test a pattern classification multi-net system based on both supervised and unsupervised learning. Following the 'divide-and-conquer' framework, the input space is partitioned into overlapping subspaces and neural networks are subsequently used to solve the respective classification subtasks. Finally, the outputs of individual classifiers are appropriately combined to obtain the final classification decision. Two clustering methods have been applied for input space partitioning and two schemes have been considered for combining the outputs of the multiple classifiers. Experiments on well-known data sets indicate that the multi-net classification system exhibits promising performance compared with the case of single network training, both in terms of error rates and in terms of training speed (especially if the training of the classifiers is done in parallel).	en
heal.publisher	SPRINGER-VERLAG	en
heal.journalName	Pattern Analysis and Applications	en
dc.identifier.doi	10.1007/s10044-002-0174-6	en
dc.identifier.isi	ISI:000183382700004	en
dc.identifier.volume	6	en
dc.identifier.issue	1	en
dc.identifier.spage	32	en
dc.identifier.epage	40	en