bSSA: Binary Salp Swarm Algorithm With Hybrid Data Transformation for Feature Selection

IEEE Access

Sandeep Kumar¹  Sayar Singh Shekhawat²  Harish Sharma²  Basit Qureshi³  Anand Nayyar⁴ 

[1] Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bengaluru, India;Department of Computer Science and Engineering, Rajasthan Technical University, Kota, India;Department of Computer Science, Prince Sultan University, Riyadh, Saudi Arabia;Graduate School, Duy Tan University, Da Nang, Vietnam;

关键词: Data transformation;    fast independent component analysis;    feature selection;    principal component analysis;    salp swarm optimizer;   

DOI  :  10.1109/ACCESS.2021.3049547

来源: DOAJ

【 摘 要 】

Feature selection is a technique commonly used in Data Mining and Machine Learning. Traditional feature selection methods, when applied to large datasets, generate a large number of feature subsets. Selecting optimal features within this high dimensional data space is time-consuming and negatively affects the system's performance. This paper proposes a new binary Salp Swarm Algorithm (bSSA) for selecting the best feature set from transformed datasets. The proposed feature selection method first transforms the original data-set using Principal Component Analysis (PCA) and fast Independent Component Analysis (fastICA) based hybrid data transformation methods; next, a binary Salp Swarm optimizer is used for finding the best features. The proposed feature selection approach improves accuracy and eliminates the selection of irrelevant features. We validate our technique on fifteen different benchmark data sets. We conduct an extensive study to measure the performance and feature selection accuracy of the proposed technique. The proposed bSSA is compared to Binary Genetic Algorithm (bGA), Binary Binomial Cuckoo Search (bBCS), Binary Grey Wolf Optimizer (bGWO), Binary Competitive Swarm Optimizer (bCSO), and Binary Crow Search Algorithm (bCSA). The proposed method attains a mean accuracy of 95.26% with 7.78% features on PCA-fastICA transformed datasets. The results show that bSSA outperforms the existing methods for the majority of the performance measures.

【 授权许可】

Unknown