Jung-Ho Kim, Joo-Ho In, Soo-Hoan Chae, Journal of Internet Computing and Services, Vol. 13, No. 4, pp. 1-10, Aug. 2012
10.7472/jksii.2012.13.4.1, Full Text:
Keywords: Classification, Feature selection, Latent Semantic Analysis, genetic algorithm, Support Vector Machine

Abstract

In this paper, an optimal feature selection method considering sematic of features, which is preprocess of document classification is proposed. The feature selection is very important part on classification, which is composed of removing redundant features and selecting essential features. LSA (Latent Semantic Analysis) for considering meaning of the features is adopted. However, a supervised LSA which is suitable method for classification problems is used because the basic LSA is not specialized for feature selection. We also apply GA (Genetic Algorithm) to the features, which are obtained from supervised LSA to select better feature subset. Finally, we project documents onto new selected feature subset and classify them using specific classifier, SVM (Support Vector Machine). It is expected to get high performance and efficiency of classification by selecting optimal feature subset using the proposed hybrid method of supervised LSA and GA. Its efficiency is proved through experiments using internet news classification with low features.

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