Abstract: For small samples, the common machine learning algorithms may not obtain good results as the feature dimension of small samples is often larger than the number of samples and some irrelevant or redundant features are often existed. It is an effective way to solve this problem by reducing the feature dimension through feature selection. This paper proposes a filter feature selection method based on mutual information for the small samples. First, the criterion of feature grouping based on the mutual information is defined. Both the correlations between features and the class and the redundancy among different features are considered in this criterion, according to which the features are grouped. Then those features that have maximal correlation with the class in each group will be chosen to compose a candidate feature subset. Meanwhile, it is ensured that the time complexity of this algorithm is low. After that, the feature selection method based on feature grouping is combined with Boruta algorithm to determine the optimal feature subset automatically from the candidate feature subset. In this way, the feature dimension can be reduced greatly. Compared with the five classical feature selection algorithms, experimental results on benchmark data sets demonstrate that the feature subset selected by the proposed method has better classification performance and running efficiency on three kinds of classifiers.

Key words: small samples, feature selection, mutual information, feature grouping, filter algorithm