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

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 a...

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Published in:	IEEE access Vol. 9; pp. 14867 - 14882
Main Authors:	Shekhawat, Sayar Singh, Sharma, Harish, Kumar, Sandeep, Nayyar, Anand, Qureshi, Basit
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Accuracy Algorithms Computer science Data mining Data transformation Datasets fast independent component analysis Feature extraction Feature selection Genetic algorithms Independent component analysis Machine learning Optimization Principal component analysis Principal components analysis salp swarm optimizer Search algorithms Support vector machines Transforms
ISSN:	2169-3536, 2169-3536
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Abstract	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.
AbstractList	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.
Author	Shekhawat, Sayar Singh Kumar, Sandeep Nayyar, Anand Sharma, Harish Qureshi, Basit
Author_xml	– sequence: 1 givenname: Sayar Singh orcidid: 0000-0002-4208-1780 surname: Shekhawat fullname: Shekhawat, Sayar Singh organization: Department of Computer Science and Engineering, Rajasthan Technical University, Kota, India – sequence: 2 givenname: Harish surname: Sharma fullname: Sharma, Harish organization: Department of Computer Science and Engineering, Rajasthan Technical University, Kota, India – sequence: 3 givenname: Sandeep orcidid: 0000-0003-4125-4165 surname: Kumar fullname: Kumar, Sandeep organization: Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bengaluru, India – sequence: 4 givenname: Anand orcidid: 0000-0002-9821-6146 surname: Nayyar fullname: Nayyar, Anand email: anandnayyar@duytan.edu.vn organization: Graduate School, Duy Tan University, Da Nang, Vietnam – sequence: 5 givenname: Basit orcidid: 0000-0001-7389-519X surname: Qureshi fullname: Qureshi, Basit email: qureshi@psu.edu.sa organization: Department of Computer Science, Prince Sultan University, Riyadh, Saudi Arabia
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SubjectTerms	Accuracy Algorithms Computer science Data mining Data transformation Datasets fast independent component analysis Feature extraction Feature selection Genetic algorithms Independent component analysis Machine learning Optimization Principal component analysis Principal components analysis salp swarm optimizer Search algorithms Support vector machines Transforms
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Title	bSSA: Binary Salp Swarm Algorithm With Hybrid Data Transformation for Feature Selection
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