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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Bibliographic Details
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
Online Access:Get full text
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Summary: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.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3049547