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Feautre Selection Algorithm for High-dimensional Biomedical Data using Information Gain and Improved Chemical Reaction Optimzation

Author(s):

Ge Zhang, Pan Yu , Jianlin Wang* and Chaokun Yan*  

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 many irrelevant or redundant information, which leads to confuse during diagnosis. Feature selection is a solution that consists in 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 information gain (IG) approach, called IGICRO.

Method: IG is adopted to obtain some important feature subsets. 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 on eight publicly 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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