Boosting simple decision trees with Bayesian learning for text categorization
Proceedings of the World Congress on Intelligent Control and Automation (WCICA)（2002）
摘要
Introduces a Bayesian method to select best base classifiers for a boosting algorithm that is used for solving text categorization problems. This method is specifically shaped for an improved version of AdaBoost.MH, an effective multi-class multi-label text classification algorithm. The paper also proposes a method to facilitate its convergence. Experimental results show that these changes improve not only the accuracy, but also the efficiency of boosting algorithms for text categorization.
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关键词
bayes methods,convergence,decision trees,learning (artificial intelligence),text analysis,bayesian learning,best base classifiers,boosting algorithm,simple decision trees,text categorization,computer science,bayesian methods,learning artificial intelligence,intelligent systems,boosting,classification algorithms,decision tree,bayesian method
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