Abstract: In this paper,a selective ensemble learning algorithm was proposed based on hierarchical selection and dynamic updating,which can optimize the parameters of classifier with multi-thread technique and select the sub sequence set of classifiers based on hierarchical selection and dynamical information.It can solve the problem in the past for choosing classifier to ensemble learning inefficiently.In addition,divide-and-conquer strategy is employed to reduce the time cost for ensemble voting.The big voting task can be divided recursively into small child task by dichotomy,then the tasks are executed in parallel and it would conquer the voting result.Experimental results show that the selective algorithm can outperform the traditional classification algorithms on F1-Measure and AUC.

Key words: Selective ensemble learning,Divide-and-conquer,Parallel computation,Classification