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: Contribution to journalArticle

6 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
Pages (from-to)463-468
Number of pages6
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3309
Publication statusPublished - Dec 1 2004

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Classifier Combination
Text Classification
Classifiers
Projection
Linear Discriminant Function
Power Transformation
Vote
Euclidean Distance
Feature Vector
Principal Component Analysis
Normalization
Comparative Study
Principal component analysis

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Accuracy improvement of automatic text classification based on feature transformation and multi-classifier combination. / Han, Xuexian; Zu, Guowei; Ohyama, Wataru; Wakabayashi, Tetsushi; Kimura, Fumitaka.

In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 3309, 01.12.2004, p. 463-468.

Research output: Contribution to journalArticle

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