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.