Joo-Ho In, Jung-Ho Kim, Soo-Hoan Chae, Journal of Internet Computing and Services, Vol. 14, No. 5, pp. 49-58, Oct. 2013
10.7472/jksii.2013.14.5.49, Full Text:  HTML
Keywords: Document Classification, Feature selection, mixed feature set, LSA, hybrid feature selection

Abstract

A novel approach for the feature selection is proposed, which is the important preprocessing task of on-line document classification. In previous researches, the features based on information from their single population for feature selection task have been selected. In this paper, a mixed feature set is constructed by selecting features from multi-population as well as single population based on various information. The mixed feature set consists of two feature sets: the original feature set that is made up of words on documents and the transformed feature set that is made up of features generated by LSA. The hybrid feature selection method using both filter and wrapper method is used to obtain optimal features set from the mixed feature set. We performed classification experiments using the obtained optimal feature sets. As a result of the experiments, our expectation that our approach makes better performance of classification is verified, which is over 90% accuracy. In particular, it is confirmed that our approach has over 90% recall and precision that have a low deviation between categories.

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