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Robust Algorithms for Combining Multiple Term Weighting Vectors for Document Classification
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 Title & Authors
Robust Algorithms for Combining Multiple Term Weighting Vectors for Document Classification
Kim, Minyoung;
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Term weighting is a popular technique that effectively weighs the term features to improve accuracy in document classification. While several successful term weighting algorithms have been suggested, none of them appears to perform well consistently across different data domains. In this paper we propose several reasonable methods to combine different term weight vectors to yield a robust document classifier that performs consistently well on diverse datasets. Specifically we suggest two approaches: i) learning a single weight vector that lies in a convex hull of the base vectors while minimizing the class prediction loss, and ii) a mini-max classifier that aims for robustness of the individual weight vectors by minimizing the loss of the worst-performing strategy among the base vectors. We provide efficient solution methods for these optimization problems. The effectiveness and robustness of the proposed approaches are demonstrated on several benchmark document datasets, significantly outperforming the existing term weighting methods.
Machine learning;Document/text classification;Term weighting;Optimization;
 Cited by
Simultaneous Learning of Sentence Clustering and Class Prediction for Improved Document Classification, The International Journal of Fuzzy Logic and Intelligent Systems, 2017, 17, 1, 35  crossref(new windwow)
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