Classification decision combination for text categorization: An experimental study

YX Bi, D Bell, Hui Wang, GD Guo, Werner Dubitzky

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

4 Citations (Scopus)

Abstract

This study investigates the combination of four different classification methods for text categorization through experimental comparisons. These methods include the Support Vector Machine, kNN (nearest neighbours), kNN model-based approach (kNNM), and Rocchio methods. We first review these learning methods and the method for combining the classifiers, and then present some experimental results on a benchmark data collection of 20-newsgroup with an emphasis of average group performance - looking at the effectiveness of combining multiple classifiers on each category. In an attempt to see why the combination of the best and the second best classifiers can achieve better performance, we propose an empirical measure called closeness as a basis of our experiments. Based on our empirical study, we verify the hypothesis that when a classifier has the high closeness to the best classifier, their combination can achieve the better performance.
Original languageEnglish
Title of host publicationUnknown Host Publication
Pages222-231
Number of pages10
Publication statusPublished - 2004

Publication series

NameLECTURE NOTES IN COMPUTER SCIENCE

Bibliographical note

15th International Conference on Database and Expert Systems Applications (DEXA 2004), Zaragoza, SPAIN, AUG 30-SEP 03, 2004; DATABASE AND EXPERT SYSTEMS APPLICATIONS, PROCEEDINGS ; Conference date: 01-01-2004

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