Combining Classifiers Based on Two-phase Ensemble Learning

In order to learn ensembled function and improve classification performance,a new algorithm framework named TPEL(Two-Phases Ensemble Learning) is proposed.For the task of email filtering,a typical problem of two-class categorization, we conduct a series of experiments on four public available datasets.The experimental results show that firstly the performance of TPEL is faintly affected by the count of the combined classifiers.Secondly,TPEL bears the best capacity when it combines multiple heterogeneous classifiers.Thirdly,in most of the experiments,the performance of TPEL is better than that of the comparing algorithms such as Nalïve Bayes,Bagging,Boosting etc.In addition,TPEL reveals its promising results in the situation of that either the weak learner is steady or not.At last,TPEL is provided with reasonable time complexity.