A Text Classification Method Based on Term Frequency Classifier Ensemble

In this paper, a method of text classification based on term frequency classifier ensemble is proposed. Term frequency classifier is a kind of simple classifier obtained after calculating terms' frequency of texts in the corpus. Though the generalization ability of term frequency classifier is not strong enough, it is a qualified base learner for ensemble because of its low computational cost, flexibility in updating with new samples and classes, and the feasibility of improving generalization with the help of ensemble paradigms. An improved AdaBoost algorithm is used to build the ensemble, which employs a scheme of compulsive weights updating to avoid early stop. Therefore it is more suitable for text classification. Experimental results on the corpus of Reuters-21578 show that the proposed method can achieve good performance in text classification tasks.