Please use this identifier to cite or link to this item: https://doi.org/10.1109/CEC.2008.4631290
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dc.titleDimension reduction using evolutionary support vector machines
dc.contributor.authorAng, J.H.
dc.contributor.authorTeoh, E.J.
dc.contributor.authorTan, C.H.
dc.contributor.authorGoh, K.C.
dc.contributor.authorTan, K.C.
dc.date.accessioned2014-10-07T04:43:25Z
dc.date.available2014-10-07T04:43:25Z
dc.date.issued2008
dc.identifier.citationAng, J.H., Teoh, E.J., Tan, C.H., Goh, K.C., Tan, K.C. (2008). Dimension reduction using evolutionary support vector machines. 2008 IEEE Congress on Evolutionary Computation, CEC 2008 : 3634-3641. ScholarBank@NUS Repository. https://doi.org/10.1109/CEC.2008.4631290
dc.identifier.isbn9781424418237
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/83631
dc.description.abstractThis paper presents a novel approach of hybridizing two conventional machine learning algorithms for dimension reduction. Genetic Algorithm (GA) and Support Vector Machines (SVMs) are integrated effectively based on a wrapper approach. Specifically, the GA component searches for the best attribute set using principles of evolutionary process, after which the reduced dataset is presented to the SVMs. Simulation results show that GA-SVM hybrid is able to produce good classification accuracy and a high level of consistency. In addition, improvements are made to the hybrid by using a correlation measure between attributes as a fitness measure to replace the weaker members in the population with newly formed chromosomes. This correlation measure injects greater diversity and increases the overall fitness of the population. © 2008 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/CEC.2008.4631290
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/CEC.2008.4631290
dc.description.sourcetitle2008 IEEE Congress on Evolutionary Computation, CEC 2008
dc.description.page3634-3641
dc.identifier.isiut000263406502064
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