Abstract The feature relevance measures employed by current feature selection methods can effectively evaluate the relevance between two features,but they do not consider the influence of the other features on them. On the premise of considering feature interaction overall,sparse representation coefficient is proposed as a feature relevance measure. The difference between the proposed method and the existing relevance measures is that it reveals the relevance between feature and target under the influence of the other features,which reflects feature interaction. In order to verify the effectiveness of sparse representation coefficient to measure relevance of feature,the classification performance is compared among feature subsets selected by Relief F and the feature selection methods using sparse coefficient,symmetrical uncertainty and Pearson correlation coefficient as relevance measures respectively. The experimental results show that the classification performance of the features selected by the proposed method is higher and more stable.
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