关键词: 高维数据, 费希尔得分, 搜索策略, 特征选择

Abstract: Feature selection is an effective technique for analyzing high-dimensional data. To improve the performance of traditional feature selection methods, a novel criterion function named minimum redundancy and maximum separability for feature selection is proposed by combining the F-score and mutual?information. Based on the new criterion function, the features select own a better ability for classification and prediction. Binary cuckoo search algorithm and quadratic programming algorithm are adopted to search the optimal subset of features, the accuracy and the?amount?of?computations for feature selection of these two search strategies are analyzed. Finally, the effectiveness of the proposed principle is verified by the experimental results though conducting tests on UCI datasets.

Key words: high-dimensional data, F-score, search strategy, feature selection