Accuracy improvement of automatic text classification based on feature transformation and multi-classifier combination

Xuexian Han, Guowei Zu, Wataru Ohyama, Tetsushi Wakabayashi, Fumitaka Kimura

Research output: Chapter in Book/Report/Conference proceedingChapter

7 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. In order to improve the classification accuracy, the multi-classifier combination by majority vote was employed.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsChi-Hung Chi, Kwok-Yan Lam
PublisherSpringer Verlag
Pages463-468
Number of pages6
ISBN (Print)3540238980, 9783540238980
DOIs
Publication statusPublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3309
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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