一种改进的基于条件互信息的特征选择算法

目前在文本分类领域较常用到的特征选择算法中，仅仅考虑了特征与类别之间的关联性，而对特征与特征之间的关联性没有予以足够的重视，这导致了特征之间预测能力的相互削弱，无法选出最有效的特征。提出了一种新的用于文本分类的特征选择算法（CMIM）,它可以帮助选出区分能力强、弱相关的特征。经实验验证，CMIM比传统的特征选择算法具有更好的性能。

Improved feature selection algorithm with conditional mutual information

Traditional feature selection algorithms have a common drawback, i.e. they do not consider the mutual relationships between features. It can result in that one feature's predictive power is weakened by others and the lost of efficiency. In this paper, we proposed a new feature selection method called Conditional Mutual Information Maximin (CMIM).It can select a set of individually discriminating and weakly dependent features. Simulation results demonstrate that the proposed method can improve the precision of text classification.