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One of the major concerns of biomedical datasets is high dimensionality. These dimensions may include irrelevant and redundant features that adversely affect the performance of classification algorithms. Extensive research has been done in the area of machine learning to handle high dimensionality. In the literature, feature selection algorithms have been developed for this purpose. In this paper, a hybrid nature-inspired algorithm is proposed which is a combination of whale optimization algorithm and genetic algorithm for feature selection. The proposed algorithm is applied to four microarray datasets and one DNA sequence dataset and compared with classical feature selection methods. In all the algorithms, decision tree classifier is mainly employed. To reach an approximate best solution and remove local solutions, the exploitation and exploration phases are balanced efficiently in the proposed algorithm. The convergence speed in the proposed algorithm is accelerated by the adaptive mechanisms. Overall, results employ better performance on the majority of datasets.

A Hybrid Whale Genetic Algorithm for Feature Selection in Biomedical Dataset