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中文摘要：
针对目前人们对分类性能的高要求和多分类器集成实现的复杂性，从基分类器准确率和基分类器间差异性两方面出发，提出了一种新的多分类器选择集成算法。该算法首先从生成的基分类器中选择出分类准确率较高的，然后利用分类器差异性度量来选择差异性大的高性能基分类器，在分类器集成之前先对分类器集进行选择获得新的分类器集。在UCI数据库上的实验结果证明，该方法优于bagging方法，取得了很好的分类识别效果。
英文摘要：
Because of the high request to classifies performance of people and the implementation complexity of multiple classifiers ensemble approach,this paper proposes an new method of selective multiple classifiers ensemble which considers of the accuracy of individual classifier and diversity among individual classifiers.This algorithm first chooses the more accuracy classifies from the production base,then chooses more different ones using diversity measure before integration.The resuh of the UCI database experiment demonstrate that the method is better than the Bagging method,and it is very good and useful for classification.
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