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On Combining Classifier Mass Functions for Text Categorization

Bell, David A., Guan, Ji-wen W. and Bi, Yaxin (2005) On Combining Classifier Mass Functions for Text Categorization. Knowledge and Data Engineering, IEEE Transactions on, 17 (10). pp. 1307-1319. [Journal article]

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DOI: 10.1109/TKDE.2005.167

Abstract

Experience shows that different text classification methods can give different results. We look here at a way of combining the results of two or more different classification methods using an evidential approach. The specific methods we have been experimenting with in our group include the support vector machine, kNN (nearest neighbors), kNN model-based approach (kNNM), and Rocchio methods, but the analysis and methods apply to any methods. We review these learning methods briefly, and then we describe our method for combining the classifiers. In a previous study, we suggested that the combination could be done using evidential operations and that using only two focal points in the mass functions gives good results. However, there are conditions under which we should choose to use more focal points. We assess some aspects of this choice from an reasoning perspective and suggest a refinement of the approach.

Item Type:Journal article
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:25511
Deposited By: Dr Yaxin Bi
Deposited On:13 Sep 2013 14:06
Last Modified:13 Sep 2013 14:06

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