کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
385792 | 660872 | 2011 | 10 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Two-level hierarchical combination method for text classification Two-level hierarchical combination method for text classification](/preview/png/385792.png)
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.
Research highlights
► A novel algorithm by combining SVM and KNN classifier based on VPRS is proposed.
► The proposed algorithm overcomes the weakness of SVM and KNN.
► The proposed algorithm outperforms state-of-the-art machines learning methods.
Journal: Expert Systems with Applications - Volume 38, Issue 3, March 2011, Pages 2030–2039