中文摘要：

为了减少表现差的个体分类器对集成器分类性能的影响,提高集成器分类效果及稳定性,提出了基于信息增益的分类器选择方法。该方法将高维分类器空间压缩至低维分类器空间,并在该空间内学习集成器。在多个数据集上的比较实验结果表明,该方法可行,其集成性能较理想。

英文摘要：

In order to eliminate adverse effect brought by bad classifiers and improve effect and stability of combiners,an ap- proach extracting classifiers based on Information Gain（IG） is proposed.It can reduce classifier space with high dimension,and then learn a combiner in lower dimension.The compared results obtain on multiple public avaiable datasets show that the method is feasible firstly.Secondly,its performance is perfect.