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Two-level hierarchical combination method for text classification
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DOI：10.1016/j.eswa.2010.07.139.png)
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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. (C) 2010 Elsevier Ltd. All rights reserved.
Keyword:
Text classification
Combination method
Variable precision rough sets
Support vector machine
k nearest neighbor
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期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
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