Stability Investigation of Improved Whale Optimization Algorithm in the Process of Feature Selection

The removal of irrelevant and insignificant features from the high-dimensional dataset is a necessary prerequisite for the exploration of information. Meta-heuristic optimization techniques have been widely used in the field of knowledge discovery over the last few years. The whale optimization algorithm (WOA) is a swarm-based metaheuristic technique that is often used in the field of dimensionality reduction. Among the various WOA-based feature selection techniques in the literature, not a single technology illuminates the stability issue of WOA. Stability is often identified as a sensitivity to the disruption of input data during the process of selecting significant features. In this study, a new feature selection model based on improved WOA (iWOA) is proposed to select significant features from a high-dimensional microarray dataset. The stability of the results obtained is evaluated with the existing stability index that satisfies all the required characteristics of the stability measure. In addition, the results of the proposed model are compared with other contemporary meta-heuristics techniques. The proposed iWOA proposes its identification as a well-stable feature selection technique according to the strength of the stability index agreement.

Automated summary

In this study, a new feature selection model based on improved WOA (iWOA) is proposed to select significant features from a high-dimensional microarray dataset. The stability of the results obtained is evaluated with the existing stability index that satisfies all the required characteristics of the stability measure.