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Pairwise optimized Rocchio algorithm for text categorization
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AI
DOI：10.1016/j.patrec.2010.09.018.png)
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Abstract
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This paper examines the Rocchio algorithm and its application in text categorization Existing approaches using global parameters optimization of Rocchio algorithm result in choosing one fixed prototype representing each category for multi-category text categorization problems Therefore they have limited discriminating power on different category s distribution and their parameter optimization methods are based on weak representation ability of the negative samples consisting of several categories We present a pairwise optimized Rocchio algorithm which dynamically adjusts the prototype position between pairs of categories Experiments were conducted on three benchmark corpora the 20-Newsgroup Reuters-21578 and TDT2 The results confirm that our proposed pairwise method achieves encouraging performance improvement over the conventional Rocchio method A comparative study with the top notch text classifier Support Vector Machine (SVM) also shows the pairwise Rocchio method achieves competitive results (C) 2010 Elsevier B V All rights reserved
Keywords:
Text categorization
Rocchio algorithm
Parameter optimization
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