Text Classification by Combining Grouping, LSA and kNN

Authors:
Naohiro Ishii;Tsuyoshi Murai;Takahiro Yamada;Yongguang Bao
Affiliations:
Aichi Institute of Technology;Aichi Institute of Technology;Aichi Institute of Technology;Aichi Institute of Technology
Venue:
ICIS-COMSAR '06 Proceedings of the 5th IEEE/ACIS International Conference on Computer and Information Science and 1st IEEE/ACIS International Workshop on Component-Based Software Engineering,Software Architecture and Reuse
Year:
2006

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Abstract

A grouping method of the similar words, is proposed for the classification of documents, which is applied to Reuters international news and it is shown that the grouping of words has equivalent ability to the Latent Semantic Analysis(LSA) in the classification accuracy. Further, a new combining method is proposed for the documents classification, which consists of Grouping, Latent Semantic Analysis 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.