Feature selection algorithm for mixed data with both nominal and continuous features

Feature selection is a crucial step in pattern recognition. Most feature selection algorithms reported are developed for continuous features. In this paper, we propose a feature selection algorithm for mixed-typed data containing both continuous and nominal features. The algorithm consists of a novel criterion for mixed feature subset evaluation and a novel search algorithm for mixed feature subset generation. The proposed feature selection algorithm is tested using both artificial and real-world problems.