Simple Decision Trees with Bayesian Learning for Text Categorization

摘要

This paper introduces a Bayesian method to select best base classifiers for 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. This 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.