关键词: 差异性, 泛化性能, 决策树, 神经网络

Abstract: Diversity among base classifiers is known to be an important factor for improving generalization performance in ensemble learning.Base classifiers are trained by randomly taking data subset as train set and using entropy diversity measure，and ensemble approaches are studied by selective strategies which include hill climbing，ensemble forward sequential selection，ensemble backward sequential selection and clustering selection.The results of experiments show that ensemble performances in general are better using the selective methods which can get classifiers of higher values of diversity.And hill climbing is superior to ensemble forward sequential selection and ensemble backward sequential selection.In addition，when the accuracy of ensemble models is stable，there is little change for value of diversity measure.Meanwhile，the number of clusters also impacts on the ensemble performance and diversity of ensemble models.

Key words: diversity, generalization performance, decision tree, neural network