Ensemble based on GA wrapper feature selection

Authors:
Enzhe Yu;Sungzoon Cho
Affiliations:
Consulting Division, Samsung Data System, Chung-Ku, Seoul, Republic of Korea;Department of Industrial Engineering, Seoul National University, Shillim Dong, KwanAk Gu, Seou, Republic of Korea
Venue:
Computers and Industrial Engineering - Special issue: Computational intelligence and information technology applications to industrial engineering selected papers from the 33 rd ICC&IE
Year:
2006

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Abstract

Ensemble of classifiers is a learning paradigm where many classifiers are jointly used to solve a problem. Research has shown that ensemble is very effective for classification tasks. Diversity and accuracy are two basic requirements for the ensemble creation. In this paper, we propose an ensemble creation method based on GA wrapper feature selection. Preliminary experimental results on real-world data show that the proposed method is promising, especially when the number of training data is limited.