Classification by weighting, similarity and kNN

Authors:
Naohiro Ishii;Tsuyoshi Murai;Takahiro Yamada;Yongguang Bao
Affiliations:
Aichi Institute of Technology, Yakusacho, Toyota, Japan;Aichi Institute of Technology, Yakusacho, Toyota, Japan;Aichi Institute of Technology, Yakusacho, Toyota, Japan;Aichi Institute of Technology, Yakusacho, Toyota, Japan
Venue:
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Year:
2006

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Abstract

In this paper, the grouping method of the similar words, is proposed for the classification of documents. It is shown that the grouping of words has equivalent ability to the LSA in the classification accuracy. Further, a new combining method is proposed for the documents classification, which consists of Grouping, Latent Semantic Analysis(LSA) followed by the k-Nearest Neighbor classification ( k-NN ). The combining method proposed here, shows the higher accuracy in the classification than the conventional methods of the kNN, and the LSA followed by the kNN. Thus, the grouping method is effective as a preprocessing before the conventional method.