Spatial bound whale optimization algorithm: an efficient high-dimensional feature selection approach

Selecting a subset of candidate features is one of the important steps in the data mining process. The ultimate goal of feature selection is to select an optimal number of high-quality features that can maximize the performance of the learning algorithm. However, this problem becomes challenging when the number of features increases in a dataset. Hence, advanced optimization techniques are used these days to search for the optimal feature combinations. Whale Optimization Algorithm (WOA) is a recent metaheuristic that has successfully applied to different optimization problems. In this work, we propose a new variant of WOA (SBWOA) based on spatial bounding strategy to play the role of finding the potential features from the high-dimensional feature space. Also, a simplified version of SBWOA is introduced in an attempt to maintain a low computational complexity. The effectiveness of the proposed approach was validated on 16 high-dimensional datasets gathered from Arizona State University, and the results are compared with the other eight state-of-the-art feature selection methods. Among the competitors, SBWOA has achieved the highest accuracy for most datasets such as TOX_171, Colon, and Prostate_GE. The results obtained demonstrate the supremacy of the proposed approaches over the comparison methods.

Automated summary

Feature selection is crucial in data mining to optimize the performance of learning algorithms by selecting high-quality features. As the number of features increases, finding the optimal feature combinations becomes challenging, necessitating the use of advanced optimization techniques like the Whale Optimization Algorithm.