BHGSO:Binary Hunger Games Search Optimization Algorithm for Feature Selection Problem

作     者：R.Manjula Devi M.Premkumar Pradeep Jangir B.Santhosh Kumar Dalal Alrowaili Kottakkaran Sooppy Nisar 

作者机构：Department of Computer Science and EngineeringKongu Engineering CollegePerundurai638060Tamil NaduIndia Department of Electrical and Electronics EngineeringDayananda Sagar College of EngineeringBengaluru560078KarnatakaIndia Rajasthan Rajya Vidyut Prasaran NigamSikar332025RajasthanIndia Department of Computer Science and EngineeringGuru Nanak Institute of TechnologyHyderabad501506TelanganaIndia Mathematics DepartmentCollege of ScienceJouf UniversitySakakaP.O.Box:2014Saudi Arabia Department of MathematicsCollege of Arts and SciencesPrince Sattam bin Abdulaziz UniversityWadi Aldawaser11991Saudi Arabia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第70卷第1期

页      面：557-579页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Binary optimization feature selection machine learning hunger games search optimization 

摘      要：In machine learning and data mining,feature selection(FS)is a traditional and complicated optimization *** the run time increases exponentially,FS is treated as an NP-hard *** researcher’s effort to build a new FS solution was inspired by the ongoing need for an efficient FS framework and the success rates of swarming outcomes in different optimization *** paper presents two binary variants of a Hunger Games Search Optimization(HGSO)algorithm based on V-and S-shaped transfer functions within a wrapper FS model for choosing the best features from a large *** proposed technique transforms the continuous HGSO into a binary variant using V-and S-shaped transfer functions(BHGSO-V and BHGSO-S).To validate the accuracy,16 famous UCI datasets are considered and compared with different state-of-the-art metaheuristic binary *** findings demonstrate that BHGSO-V achieves better performance in terms of the selected number of features,classification accuracy,run time,and fitness values than other state-of-the-art *** results demonstrate that the BHGSO-V algorithm can reduce dimensionality and choose the most helpful features for classification *** proposed BHGSO-V achieves 95%average classification accuracy for most of the datasets,and run time is less than 5 *** low and medium dimensional datasets and less than 10 sec for high dimensional datasets.