Time-varying hierarchical chains of salps with random weight networks for feature selection

作     者：Faris, Hossam Heidari, Ali Asghar Al-Zoubi, Ala' M. Mafarja, Majdi Aljarah, Ibrahim Eshtay, Mohammed Mirjalili, Seyedali 

作者机构：Univ Jordan King Abdullah II Sch Informat Technol Amman Jordan Univ Tehran Sch Surveying & Geospatial Engn Tehran Iran Birzeit Univ Dept Comp Sci Birzeit Palestine Torrens Univ Australia Brisbane Qld 4006 Australia Natl Univ Singapore Sch Comp Dept Comp Sci Singapore Singapore 

出 版 物：《EXPERT SYSTEMS WITH APPLICATIONS》 (专家系统及其应用)

年 卷 期：2020年第140卷

页      面：112898-000页

核心收录：

学科分类：1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Feature selection Salp swarm algorithm Optimization Evolutionary algorithms 

摘      要：Feature selection (FS) is considered as one of the most common and challenging tasks in Machine Learning. FS can be considered as an optimization problem that requires an efficient optimization algorithm to find its optimal set of features. This paper proposes a wrapper FS method that combines a time-varying number of leaders and followers binary Salp Swarm Algorithm (called TVBSSA) with Random Weight Network (RWN). In this approach, the TVBSSA is used as a search strategy, while RWN is utilized as an induction algorithm. The objective function is formulated in a manner to aggregate three objectives: maximizing the classification accuracy, maximizing the reduction rate of the selected features, and minimizing the complexity of generated RWN models. To assess the performance of the proposed approach, 20 well-known UCI datasets and a number of existing FS methods are employed. The comparative results show the ability of the proposed approach in outperforming similar algorithms in the literature and its merits to be used in systems that require FS. (C) 2019 Elsevier Ltd. All rights reserved.