【摘要】 特征选择在模式识别技术中起着非常重要的作用,已有多种特征选择的方法,但用信息论的方法进行特征选择还是一个新的课题.MIFS算法和MIFS-U算法都是近似算法,随着输入特征的增加,特征选择性能逐渐下降.本文通过研究这两种算法,提出一种改进方法,在运算量几乎不增加的情况下,提高这两种算法的特征选择性能.更多还原

【Abstract】 Feature selection plays an important role in classification problems such as pattern recognition, there are a lot of methods to select feature, but the methods based on information theory to select feature is a new subject yet.MIFS methods and MIFS-U methods are all approximate methods, their feature selection performance will be decreased with the increasing of selected feature number. This paper describes a method to modify the two kinds of methods to improve their feature selection performance without more calculation increasing.更多还原