Feature selection algorithm for data with both nominal and continuous features

Authors:
Wenyin Tang;Kezhi Mao
Affiliations:
School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore;School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore
Venue:
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Year:
2005

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Abstract

Wrapper and filter are two commonly used feature selection schemes. Because of its computational efficiency, the filter method is often the first choice when dealing with large dataset. However, most of filter methods reported in the literature are developed for continuous feature selection. In this paper, we proposed a filter method for mixed data with both continuous and nominal features. The new algorithm includes a novel criterion for mixed feature evaluation, and a novel search algorithm for mixed feature subset generation. The proposed method is tested using a few benchmark real-world problems.