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

作　　者：Abdelaziz I.Hammouri Mohammed A.Awadallah Malik Sh.Braik Mohammed Azmi Al-Betar Majdi Beseiso 

机构地区：[1]Department of Computer Science,Al-Balqa Applied University,Al-Salt,19117,Jordan [2]Department of Scientific Research and Graduate Studies,University of Prince Mugrin,42241,Medina,Saudi Arabia [3]Department of Computer Science,Al-Aqsa University,Gaza,4051,Palestine [4]Artificial Intelligence Research Center(AIRC),Ajman University,Ajman,United Arab Emirates [5]Department of Information Technology,Al-Huson University College,Al-Balqa Applied University,Al-Huson,Irbid,21110,Jordan

出　　处：《Journal of Bionic Engineering》2024年第4期2000-2033,共34页仿生工程学报（英文版）

基　　金：supported by the Deanship of Scientific Research and Innovation at Al-Balqa Applied University in Jordan.

摘　　要：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.

关 键 词：Dwarf mongoose optimization algorithm Optimization Feature selection CLASSIFICATION 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]