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

Bi, Yaxin, Bell, David A., Wang, Hui, Guo, Gongde and Greer, Kieran (2004) Combining Multiple Classifiers Using Dempster's Rule of Combination for Text Categorization. In: Modeling Decisions for Artificial Intelligence Lecture Notes in Computer Science. Springer Berlin Heidelberg, pp. 127-138. ISBN 978-3-540-22555-3 [Book section]

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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.

Item Type:Book section
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
ID Code:25518
Deposited By: Dr Yaxin Bi
Deposited On:20 Jan 2016 15:37
Last Modified:20 Jan 2016 15:37

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