Feature selection is one of the most important issues in data mining, machine learning and pattern recognition. Aiming at the problem of preference of traditional information gain algorithm in feature selection when the class and feature are unevenly distributed, this paper proposed a new feature selection algorithm based on information gain ratio and random forest. The proposed algorithm combined with the advantages of filter and wrapper modes. Firstly, it carried out a comprehensive measurement of features from two aspects of information correlation and classification ability. Secondly, it used sequential forward selection(SFS) strategy to select the features, and used the classification accuracy as the evaluation index to measure the feature subset. Finally, it obtained the optimal feature subset. The experimental results show that the proposed algorithm can not only achieve the effect of dimension reduction in feature space, but also effectively improve the classification performance and recall rate of classification algorithm.

[2019-07-05] Printed Article /doc/2018.01.0024/v2