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CIKM
2005
Springer

A novel refinement approach for text categorization

9 years 9 months ago
A novel refinement approach for text categorization
In this paper we present a novel strategy, DragPushing, for improving the performance of text classifiers. The strategy is generic and takes advantage of training errors to successively refine the classification model of a base classifier. We describe how it is applied to generate two new classification algorithms; a Refined Centroid Classifier and a Refined Naïve Bayes Classifier. We present an extensive experimental evaluation of both algorithms on three English collections and one Chinese corpus. The results indicate that in each case, the refined classifiers achieve significant performance improvement over the base classifiers used. Furthermore, the performance of the Refined Centroid Classifier implemented is comparable, if not better, to that of state-of-the-art support vector machine (SVM)-based classifier, but offers a much lower computational cost. Categories and Subject Descriptors H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval-search process; I...
Songbo Tan, Xueqi Cheng, Moustafa Ghanem, Bin Wang
Added 26 Jun 2010
Updated 26 Jun 2010
Type Conference
Year 2005
Where CIKM
Authors Songbo Tan, Xueqi Cheng, Moustafa Ghanem, Bin Wang, Hongbo Xu
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