Abstract:

Plenty of redundant features may reduce the performance of data classification in massive dataset, so a new method of automatic feature selection based on the integration of Mutual Information and Fuzzy C-Means (FCM) clustering, named FCC-MI, was proposed to resolve this problem. Firstly, MI and its correlation function were analyzed, then the features were sorted according to the correlation value. Secondly, the data was grouped according to the feature with the maximum correlation, and the number of the optimal features were determined automatically by FCM clustering method. At last, the optimization selection of the features was performed using correlation value. Experiments on seven datasets of UCI machine learning database were conducted to compare FCC-MI with three methods come from the literatures, including WCMFS (Within class variance and Correlation Measure Feature Selection), B-AMBDMI (Based on Approximating Markov Blank and Dynamic Mutual Information), and T-MI-GA (Two-stage feature selection algorithm based on MI and GA). The theoretical analysis and experimental results show that the proposed method not only improves the efficiency of data classification, but also ensures the classification accuracy and automatically determine the optimal feature subset, which reduces the number of the features of the dataset, thus it is suitable for feature reduction and analysis of mass data with large correlation features.