关键词: 特征选择, 聚类, 随机森林, 多特征置换

Abstract:

Aiming at the problem of calculating high consumption of traditional random forest with the increase of feature number, a multi-feature permutation algorithm by random forest is proposed. All of features are clustered firstly, then the features in the same cluster are taken random permutation as the other clusters remain unchanged. The importance of all the feature-clusters are calculated and ranked. The feature in the same cluster is ranked by the correlation of itself and classification information. A correlation threshold is used to choose the important features. The rule of ranking the remaining feature is first between clusters, then within clusters. To further illustrate the effectiveness of the method, three correspondingly multi-feature permutation algorithms by random forest are designed based on K-mean, hierarchical and fuzzy C-mean clustering algorithms. The experimental results show that the proposed algorithm achieves higher classification accuracy with fewer features and higher time efficiency compared with the traditional random forest method.

Key words: feature selection, cluster, random forest, multi-feature permutation