%0 Journal Article %A LIN Song %A WANG Kaijun %A ZENG Yuanpeng %T Interference entropy feature selection method for two-class distinguishing ability %D 2020 %R 10.11772/j.issn.1001-9081.2019071200 %J Journal of Computer Applications %P 626-630 %V 40 %N 3 %X Aiming at the existing feature selection methods lacking the ability to measure the overlap/separation of different classes of data, an Interference Entropy of Two-Class Distinguishing (IET-CD) method was proposed to evaluate the two-class distinguishing ability of features. For the feature containing two classes (positive and negative), firstly, the mixed conditional probability of the negative class samples within the range of positive class data and the probability of the negative class samples belonging to the positive class were calculated; then, the confusion probability was calculated by the mixed conditional probability and attribution probability, and the confusion probability was used to calculate the positive interference entropy. In the similar way, the negative interference entropy was calculated. Finally, the sum of positive and negative interference entropies was taken as the two-class interference entropy of the feature. The interference entropy was used to evaluate the distinguishing ability of the feature to the two-class sample. The smaller the interference entropy value of the feature, the stronger the two-class distinguishing ability of the feature. On three UCI datasets and one simulated gene expression dataset, five optimal features were selected for each method, and the two-class distinguishing ability of the features were compared, so as to compare the performance of the methods. The experimental results show that the proposed method is equivalent or better than the NEFS (Neighborhood Entropy Feature Selection) method, and compared with the Single-indexed Neighborhood Entropy Feature Selection (SNEFS), feature selection based on Max-Relevance and Min-Redundancy (MRMR), Joint Mutual Information (JMI) and Relief method, the proposed method is better in most cases. The IET-CD method can effectively select features with better two-class distinguishing ability. %U http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2019071200