关键词: 互信息, 特征选择, 熵

Abstract: Conventional mutual-information-based feature selection algorithms seldom considers how features work together，with the features incresement，the computational complexity of the algorithms will increase dramatically.So propose mutual-
information-based evaluation function for feature subset selected，different from other mutual-information-based feature selection algorithm，it considers how features work together.So it produces the optimal feature subset and improves the classification accuracy of classifier.The computational complexity of it is also limited.The results show about 3%～5% increase for classification accuracy and 25%～30% improvement for error reduction rate compare with other conventional mutual-information-
based feature selection algorithms.

Key words: mutual information, feature selection, entropy