Abstract: Feature weighting can provide more comprehensive information to enhance models learning ability and decision accuracy,but the relationship between features and classifiers is often ignored in practice.To address this problem,a classifier-oriented feature weighting method called COFW is proposed and applied to Android malware detection.Firstly,the features of seven categories are extract- ed from the Android application package,and the most important feature subset is selected.Secondly, according to the classifier used to detect malware,COFW is employed to compute the optimal weight of each feature for the classifier.Finally,the classifier is trained on  the weighted features.COFW adopts the method of removing one to calculate an initial weight for each feature,then maps it to the final weight through a mapping function,and uses a differential evolution algorithm to optimize the parameters of the mapping function and the classifier.The experimental results show that using COFW for feature weighting can improve the performance of the classifier,and COFW outperforms the other four feature weighting methods designed for Android malware detection.

Key words: feature weighting, classifier-oriented, mapping function, Android malware detection