Classification decision combination for text categorization: An experimental study

YX Bi, D Bell, H 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
Place of PublicationHEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
Pages222-231
Number of pages10
Publication statusPublished - 2004
EventDATABASE AND EXPERT SYSTEMS APPLICATIONS, PROCEEDINGS - Zaragoza, Spain
Duration: 1 Jan 2004 → …

Publication series

NameLECTURE NOTES IN COMPUTER SCIENCE

Conference

ConferenceDATABASE AND EXPERT SYSTEMS APPLICATIONS, PROCEEDINGS
Period1/01/04 → …

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  • Cite this

    Bi, YX., Bell, D., Wang, H., Guo, GD., & Dubitzky, W. (2004). Classification decision combination for text categorization: An experimental study. In Unknown Host Publication (pp. 222-231). (LECTURE NOTES IN COMPUTER SCIENCE)..