Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.engappai.2004.09.010
DC FieldValue
dc.titleA class decomposition approach for GA-based classifiers
dc.contributor.authorGuan, S.-U.
dc.contributor.authorZhu, F.
dc.date.accessioned2014-06-16T09:24:08Z
dc.date.available2014-06-16T09:24:08Z
dc.date.issued2005-04
dc.identifier.citationGuan, S.-U., Zhu, F. (2005-04). A class decomposition approach for GA-based classifiers. Engineering Applications of Artificial Intelligence 18 (3) : 271-278. ScholarBank@NUS Repository. https://doi.org/10.1016/j.engappai.2004.09.010
dc.identifier.issn09521976
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/53939
dc.description.abstractGenetic algorithm (GA) has been used as a conventional method for classifiers to evolve solutions adaptively for classification problems. In this paper, a new approach using class decomposition is proposed to improve the performance of GA-based classifiers. A classification problem is fully partitioned into several class modules in the output domain and each module is responsible for solving a fraction of the original problem. These modules are trained in parallel and independently and the results obtained are integrated and evolved further for a final solution. A scheme based on Fisher's linear discriminant (FLD) computation is used to estimate the difficulty of separating two classes. Based on the FLD information derived, different integration approaches are proposed and their performance is compared. The experiment results on a benchmark data set show that class decomposition can achieve higher classification rate than the normal GA and FLD-based integration improves the classification accuracy further. © 2004 Elsevier Ltd. All rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.engappai.2004.09.010
dc.sourceScopus
dc.subjectClass decomposition
dc.subjectClassification
dc.subjectGenetic algorithm
dc.typeArticle
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1016/j.engappai.2004.09.010
dc.description.sourcetitleEngineering Applications of Artificial Intelligence
dc.description.volume18
dc.description.issue3
dc.description.page271-278
dc.description.codenEAAIE
dc.identifier.isiut000228264400002
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