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

✍ Scribed by Wenyin Tang; K.Z. Mao

• Publisher: Elsevier Science
• Year: 2007
• Tongue: English
• Weight: 283 KB
• Volume: 28
• Category: Article
• ISSN: 0167-8655
• DOI: 10.1016/j.patrec.2006.10.008

✦ Synopsis

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.