关键词: 混合式, 过滤式特征排序, 封装式特征选择, 特征选择, 隐私保护, K-匿名, 前向搜索策略

Abstract:

K-anonymous algorithm makes the data reached the condition of K-anonymity by generalizing and suppressing the data. It can be seen as a special feature selection method named K-anonymous feature selection which considers both data privacy and classification performance. In K-anonymous feature selection method， the characteristics of K-anonymity and feature selection are combined to use multiple evaluation criteria to select the subset of K-anonymous features. It is difficult for the filtered K-anonymous feature selection method to search all the candidate feature subsets satisfying the K-anonymous condition， and the classification performance of the obtained feature subset cannot be guaranteed to be optimal， and the wrapper feature selection method has very high-cost calculation. Therefore， a hybrid K-anonymous feature selection method was designed by combining the characteristics of filtered feature sorting and wrapper feature selection by improving the forward search strategy in the existing methods and thereby using classification performance as the evaluation criterion to select the K-anonymous feature subset with the best classification performance. Experiments were carried out on multiple public datasets， and the results show that the proposed algorithm can outperform the existing algorithms in classification performance and has less information loss.

Key words: hybrid, filtered feature sorting, wrapper feature selection, feature selection, privacy protection, K-anonymity, forward search strategy