Zebra optimization algorithm for feature selection - Faizan, Muhammad; Muhammad Arif, Mohamad (IEEE, 2026)
Feature selection is very important part in increasing the performance of machine learning models. This is done because of reducing the complexity of dataset, improving accuracy and lowering computation costs and time. In this paper, we proposed the use of Zebra Optimization Algorithm (ZOA) for handling task regarding feature selection. This algorithm is adopted by inspiring the social behavior and movements strategies of zebras in wildlife. For checking the validation of ZOA for feature selection, several experiments were conducted on different benchmark datasets. These datasets were taken from UCI Machine Learning Repositories. The performance of ZOA was evaluated in the form of classification accuracy, number of selected features and feature reduction rate (FRR). Results show that the ZOA successfully achieved a high selection quality. This leads to increasing the classification accuracy on different datasets. This study highlights the capability of ZOA as an optimistic tool for feature selection in different real-world applications.