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

Wenyin Tang

doi:10.1016/j.patrec.2006.10.008

2007-04-01

PRE

Wenyin Tang *

Kezhi Mao

DOI：10.1016/j.patrec.2006.10.008

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摘要

En 中文

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. (c) 2006 Elsevier B.V. All rights reserved.

Keyword:

feature selection

mixed data

continuous feature

nominal feature