An improved FloatBoost algorithm for naïve bayes text classification

Abstract

Boosting is a method for supervised learning, which has successfully been applied to many different domains and has proven one of the best performers in text classification exercises so far. FloatBoost learning uses a backtrack mechanism after each iteration of AdaBoost learning to minimize the error rate directly, rather than minimizing an exponential function of the margin as in the traditional AdaBoost algorithm. This paper presents an improved FloatBoost boosting algorithm for boosting Naïve Bayes text classification, called DifBoost, which combines Divide and Conquer Principal with the FloatBoost algorithm. Integrating FloatBoost with the Divide and Conquer principal, DifBoost divides the input space into a few sub-spaces during training process and the final classifier is formed with the weighted combination of basic classifiers, where basic classifiers are affected by different sub-spaces differently. Extensive experiments using benchmarks are conducted and the encouraging results show the effectiveness of our proposed algorithm. © Springer-Verlag Berlin Heidelberg 2005.