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.
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