Title of article :
Two-level hierarchical combination method for text classification
Author/Authors :
Li، نويسنده , , Wen and Miao، نويسنده , , Duoqian and Wang، نويسنده , , Weili، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2011
Pages :
10
From page :
2030
To page :
2039
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.
Keywords :
Text classification , Variable precision rough sets , Combination method , Support vector machine , k nearest neighbor
Journal title :
Expert Systems with Applications
Serial Year :
2011
Journal title :
Expert Systems with Applications
Record number :
2348846
Link To Document :
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