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

Authors:
Wenyin Tang;K. Z. Mao
Affiliations:
School of Electrical and Electronic Engineering, Nanyang Technological University, Biomedical Instrumentation Lab, S2.1-b4-02 EEE, NTU Nanyang Avenue, Singapore 639798, Singapore;School of Electrical and Electronic Engineering, Nanyang Technological University, Biomedical Instrumentation Lab, S2.1-b4-02 EEE, NTU Nanyang Avenue, Singapore 639798, Singapore
Venue:
Pattern Recognition Letters
Year:
2007

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Abstract

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.