Two-level hierarchical combination method for text classification |
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Authors: | Wen Li Duoqian Miao Weili Wang |
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Affiliation: | 1. Department of Computer Science and Software Engineering, International Islamic University Islamabad, Pakistan;2. Department of Informatics and Telematics, Harokopio University of Athens, Greece;3. Department of Computer Science, National University of Modern Languages, Islamabad, Pakistan |
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Abstract: | Text classification has been recognized as one of the key techniques in organizing digital data. The intuition that each algorithm has its bias data and build a high performance classifier via some combination of different algorithm is a long motivation. In this paper, we proposed a two-level hierarchical algorithm that systematically combines the strength of support vector machine (SVM) and k nearest neighbor (KNN) techniques based on variable precision rough sets (VPRS) to improve the precision of text classification. First, an extension of regular SVM named variable precision rough SVM (VPRSVM), which partitions the feature space into three kinds of approximation regions, is presented. Second, a modified KNN algorithm named restrictive k nearest neighbor (RKNN) is put forward to reclassify texts in boundary region effectively and efficiently. The proposed algorithm overcomes the drawbacks of sensitive to noises of SVM and low efficiency of KNN. Experimental results compared with traditional algorithms indicate that the proposed method can improve the overall performance significantly. |
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