Efficient feature selection based on correlation measure between continuous and discrete features

Feature selection is frequently used to reduce the number of features in many applications where data of high dimensionality are involved. Lots of the feature selection methods mainly focus on measuring the correlation (or similarity) between two features. However, most correlation measures are limited to handling only certain types of data. Feature space consisting of continuous/discrete feature or their combination presents a severe challenge to feature selection in terms of efficiency and effectiveness. This paper introduces a novel approach that can measure the correlation between a continuous and a discrete feature, and then proposes an efficient filter feature selection algorithm based on correlation analysis by removing weakly relevant and irrelevant features, as well as relevant but redundant features. Both theoretical and experimental comparisons with other representative filter approaches on UCI datasets show that the proposed algorithm is effective for selecting continuous and discrete features, as well as the mixture of continuous and discrete features. The performance of ECMBF is superior to other approaches in terms of dimensionality reduction and classification error rate.