چکیده مقاله
The process of selecting features is crucial in data mining and machine learning, as it can greatly improve the performance of models Feature selection is not amenable to polynomial solutions One approach involves utilizing approximate methods and metaheuristic algorithms Metaheuristic algorithms have certain parameters that are typically treated as constants This paper explores the application of swarm based algorithms, including the Firefly Algorithm FA , Bat Algorithm BA , Pathfinder Algorithm PFA , and Grasshopper Optimization Algorithm GOA , for feature selection All of these algorithms have one or more parameters that can be updated adaptively The research focuses on the adaptive adjustment of algorithm parameters through the use of fuzzy inference systems, aiming to enhance the performance and efficiency of feature selection In this paper, classification error, and the proportion of selected features are considered objective functions A comparative analysis of the performance of these algorithms, with a specific emphasis on the impact of adaptive parameter updates, is presented The findings offer valuable insights into the use of swarm based algorithms for feature selection, providing guidance for practitioners and researchers in the field of metaheuristic optimization
کلیدواژهها
نویسندگان
Aboozar Zandvakili
Department of Computer Engineering, College of Engineering, Jiroft Branch, Islamic Azad University, Jiroft Iran
Najme Mansouri
Assistant Professor, Faculty of Shahid Bahonar University of Kerman, Kerman, Iran,
Mohammad Masoud Javidi
Associate Professor, Faculty of Shahid Bahonar University of Kerman, Kerman, Iran,
شیوه ارجاع
Zandvakili, Aboozar and Mansouri, Najme and Javidi, Mohammad Masoud,1402,Fuzzy Swarm-based Algorithm For Feature Selection,5th International Conference on Software Computing,Rudsar
ارائهشده در
مجموعه مقالات پنجمین کنفرانس بین المللی محاسبات نرم16 اسفند 1402 · رودسر