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Feature Selection Algorithm for High-dimensional Biomedical Data Using Information Gain and Improved Chemical Reaction Optimization

[ Vol. 15 , Issue. 8 ]

Author(s):

Ge Zhang, Pan Yu , Jianlin Wang* and Chaokun Yan*   Pages 912 - 926 ( 15 )

Abstract:


Background: There have been rapid developments in various bioinformatics technologies, which have led to the accumulation of a large amount of biomedical data. However, these datasets usually involve thousands of features and include much irrelevant or redundant information, which leads to confusion during diagnosis. Feature selection is a solution that consists of finding the optimal subset, which is known to be an NP problem because of the large search space.

Objective: For the issue, this paper proposes a hybrid feature selection method based on an improved chemical reaction optimization algorithm (ICRO) and an information gain (IG) approach, which called IGICRO.

Methods: IG is adopted to obtain some important features. The neighborhood search mechanism is combined with ICRO to increase the diversity of the population and improve the capacity of local search.

Results: Experimental results of eight public available data sets demonstrate that our proposed approach outperforms original CRO and other state-of-the-art approaches.

Keywords:

Feature selection, chemical reaction optimization algorithm (CRO), information gain, neighborhood search mechanism, biomedical data, optimal subset.

Affiliation:

School of Computer and Information Engineering, Henan University, Kaifeng, School of Computer and Information Engineering, Henan University, Kaifeng, School of Computer and Information Engineering, Henan University, Kaifeng, School of Computer and Information Engineering, Henan University, Kaifeng

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