Feature Selection with Variable Interaction
Hong Ge · Journal of Information and Computational Science · 2014
Feature selection is an important preprocessing procedure in machine learning. The selection of feature subset is based on the evaluation of difierent features. Relevancy and redundancy are two common-used evaluation criteria for feature selection. Variable interaction is another relationship between variables. Variables interaction also has crucial efiect on feature selection. Although there are a lot of researches on variable interaction measure, there is a little works in applying variable interaction measure to feature selection. In this paper, a novel algorithm, which implements Feature Selection with considering the Variable Interaction (FSwVI), is proposed. The proposed method uses the result of variable interaction measure to complement and improve the feature subset selected by a normal feature selection method. The empirical results show that it is necessary and efiective to select features with considering variables interaction.