Combining Multiple Classifiers Using Dempster's Rule of Combination for Text Categorization

Yaxin Bi, David A. Bell, Hui Wang, Gongde Guo, Kieran Greer

Research output: Chapter in Book/Report/Conference proceedingChapter

37 Citations (Scopus)

Abstract

In this paper, we present an investigation into the combination of four different classification methods for text categorization using Dempster’s rule of combination. These methods include the Support Vector Machine, kNN (nearest neighbours), kNN model-based approach (kNNM), and Rocchio methods. We first present an approach for effectively combining the different classification methods. We then apply these methods to a benchmark data collection of 20-newsgroup, individually and in combination. Our experimental results show that the performance of the best combination of the different classifiers on the 10 groups of the benchmark data can achieve 91.07% classification accuracy, which is 2.68% better than that of the best individual method, SVM, on average.
Original languageEnglish
Title of host publicationModeling Decisions for Artificial Intelligence Lecture Notes in Computer Science
PublisherSpringer
Pages127-138
ISBN (Print)978-3-540-22555-3
Publication statusPublished - 2004

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    Bi, Y., Bell, D. A., Wang, H., Guo, G., & Greer, K. (2004). Combining Multiple Classifiers Using Dempster's Rule of Combination for Text Categorization. In Modeling Decisions for Artificial Intelligence Lecture Notes in Computer Science (pp. 127-138). Springer.