BCDDO: Binary Child Drawing Development Optimization

TL;DR

This paper introduces BCDDO, a binary metaheuristic algorithm for feature selection that significantly improves classification accuracy across multiple datasets compared to existing methods.

Contribution

The paper proposes BCDDO, a novel binary metaheuristic algorithm for feature selection, demonstrating superior performance over existing algorithms in classification tasks.

Findings

Achieved high classification accuracy: 98.75%, 98.83%, and 99.36%.

Outperformed existing feature selection techniques.

Validated on COVID and breast cancer datasets.

Abstract

A lately created metaheuristic algorithm called Child Drawing Development Optimization (CDDO) has proven to be effective in a number of benchmark tests. A Binary Child Drawing Development Optimization (BCDDO) is suggested for choosing the wrapper features in this study. To achieve the best classification accuracy, a subset of crucial features is selected using the suggested BCDDO. The proposed feature selection technique's efficiency and effectiveness are assessed using the Harris Hawk, Grey Wolf, Salp, and Whale optimization algorithms. The suggested approach has significantly outperformed the previously discussed techniques in the area of feature selection to increase classification accuracy. Moderate COVID, breast cancer, and big COVID are the three datasets utilized in this study. The classification accuracy for each of the three datasets was (98.75, 98.83%, and 99.36) accordingly.