Accuracy improvement of automatic text classification based on feature transformation

Guowei Zu, Wataru Ohyama, Tetsushi Wakabayashi, Fumitaka Kimura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

In this paper, we describe a comparative study on techniques of feature transformation and classification to improve the accuracy of automatic text classification. The normalization to the relative word frequency, the principal component analysis (K-L transformation) and the power transformation were applied to the feature vectors, which were classified by the Euclidean distance, the linear discriminant function, the projection distance, the modified projection distance and the SVM.

Original languageEnglish
Title of host publicationProceedings of the 2003 ACM Symposium on Document Engineering
PublisherAssociation for Computing Machinery
Pages118-120
Number of pages3
ISBN (Print)1581137249, 9781581137248
DOIs
Publication statusPublished - 2003
EventProceedings of the 2003 ACM Symposium on Document Engineering - Grenoble, France
Duration: Nov 20 2003Nov 22 2003

Publication series

NameProceedings of the 2003 ACM Symposium on Document Engineering

Other

OtherProceedings of the 2003 ACM Symposium on Document Engineering
CountryFrance
CityGrenoble
Period11/20/0311/22/03

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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  • Cite this

    Zu, G., Ohyama, W., Wakabayashi, T., & Kimura, F. (2003). Accuracy improvement of automatic text classification based on feature transformation. In Proceedings of the 2003 ACM Symposium on Document Engineering (pp. 118-120). (Proceedings of the 2003 ACM Symposium on Document Engineering). Association for Computing Machinery. https://doi.org/10.1145/958238.958242