چکیده مقاله
Hundreds of variables in data lead to data with veryhigh dimensions, allowing many feature selection methods to bedeveloped The purpose of feature selection in machine learning,pattern recognition, and data mining is to choose features thatwill enhance learning performance The aim of this paper is touse the binary version of the Social Mimic Optimization SMO algorithm as Binary Social Mimic Optimization BSMO forfeature selection The combined fitness function is chosen becauseof its three main objectives: reducing classification error,balancing sensitivity and specificity, and reducing the number ofselected features The proposed method is compared with severaloptimization methods, including Binary Genetic Algorithms BGA and Particle Swarm Optimization BPSO , as well as withBinary Atom Search Optimization BASO The results of theevaluation using five UCI datasets show that the proposedmethod is superior to others for solving optimization problems
کلیدواژهها
نویسندگان
Mohammad Ansari Shiri
Department of Computer ScienceShahid Bahonar UniversityKerman, Iran
Najme Mansouri
Department of Computer ScienceShahid Bahonar UniversityKerman, Iran
شیوه ارجاع
Ansari Shiri, Mohammad and Mansouri, Najme,1401,An effective Feature Selection with Social MimicOptimization Algorithm,1st International Conference and 6th National Conference on Computers, information technology and applications of artificial intelligence
ارائهشده در
مجموعه مقالات اولین کنفرانس بین المللی و ششمین کنفرانس ملی کامپیوتر، فناوری اطلاعات و کاربردهای هوش مصنوعی3 اسفند 1401 · اهواز