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|Title:||Feature selection and classification via a GA-SVM hybrid||Authors:||Teoh, E.-J.
|Issue Date:||2008||Citation:||Teoh, E.-J., Xiang, C. (2008). Feature selection and classification via a GA-SVM hybrid. Proceedings of the 2008 International Conference on Genetic and Evolutionary Methods, GEM 2008 : 83-89. ScholarBank@NUS Repository.||Abstract:||In this article, a hybrid approach comprising of two conventional machine learning algorithms is proposed to carry out attribute selection. Genetic algorithms (GAs) and Support Vector Machines (SVMs) are integrated effectively based on a wrapper approach. Specifically, the GA component searches for the best attribute set by applying the principles of an evolutionary process. The SVM then classifies the patterns in the reduced datasets, corresponding to the attribute subsets represented by the GA chromosomes. Simulation results demonstrate that the GA-SVM hybrid produces good classification accuracy and a higher level of consistency that is comparable to other established algorithms. 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.||Source Title:||Proceedings of the 2008 International Conference on Genetic and Evolutionary Methods, GEM 2008||URI:||http://scholarbank.nus.edu.sg/handle/10635/51166||ISBN:||1601320698|
|Appears in Collections:||Staff Publications|
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