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Hierarchical Text Categorization Through a Vertical Composition of Classifiers
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Selective Ensemble Algorithms of Support Vector Machines Based on Constraint Projection
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Ensemble of feature sets and classification algorithms for sentiment classification
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Text categorization (TC), as an important domain of machine learning, has many unique traits, such as huge number of features, serious redundant features, dataset imbalance, etc. In this paper the various ensemble methods of naïve Bayes classifiers and SVM classifiers are experimentally compared on the TC tasks. Besides, a new type of classifiers, moderated asymmetric naïve Bayes classifiers, is proposed. Its advantages over the conventional naïve Bayes classifiers in performance and computational efficiency are demonstrated.