Adaptive adjustment weighted text classification

To improve the performance of the naive Bayes classifier, a method is proposed which regulates text categories by adding adjustment values to the output of the naive Bayes classifier. The classification pattern was learned in an incremental and adaptive way, and the interval during which the output of the naive Bayes classifier should be adjusted was built according to the classification performance evaluated by historical outputs. Then the adjustment value was adaptively added to the output of the naive Bayes classifier distributed in the interval to regulate its category. The experiment results on Trec05，Trec06，Trec07，CEAS08 datasets show that the proposed method outperforms the naive Bayes classifier and the bagging naive Bayes classifier in terms of accuracy, Macro F1, in addition to its simplicity and practicality.