Improved Dwarf Mongoose Optimization Algorithm for Feature Selection: Application in Software Fault Prediction Datasets

Abstract

Feature selection (FS) plays a crucial role in pre-processing machine learning datasets, as it eliminates redundant features to improve classification accuracy and reduce computational costs. This paper presents an enhanced approach to FS for software fault prediction, specifically by enhancing the binary dwarf mongoose optimization (BDMO) algorithm with a crossover mechanism and a modified positioning updating formula. The proposed approach, termed iBDMOcr, aims to fortify exploration capability, promote population diversity, and lastly improve the wrapper-based FS process for software fault prediction tasks. iBDMOcr gained superb performance compared to other well-esteemed optimization methods across 17 benchmark datasets. It ranked first in 11 out of 17 datasets in terms of average classification accuracy. Moreover, iBDMOcr outperformed other methods in terms of average fitness values and number of selected features across all datasets. The findings demonstrate the effectiveness of iBDMOcr in addressing FS problems in software fault prediction, leading to more accurate and efficient models.