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On Combining Classifier Mass Functions for Text Categorization
October 2005 (vol. 17 no. 10)
pp. 1307-1319
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 [1] and that using only two focal points in the mass functions (see below) 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 evidential reasoning perspective and suggest a refinement of the approach.
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Index Terms:
Index Terms- Data mining systems and tools, modeling of structured, textual and multimedia data, uncertainty reasoning.
Citation:
David A. Bell, J.W. Guan, Yaxin Bi, "On Combining Classifier Mass Functions for Text Categorization," IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 10, pp. 1307-1319, Oct. 2005, doi:10.1109/TKDE.2005.167
