bSSA: Binary Salp Swarm Algorithm With Hybrid Data Transformation for Feature Selection Article Swipe
Sayar Singh Shekhawat
,
Harish Sharma
,
Sandeep Kumar
,
Anand Nayyar
,
Basit Qureshi
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/access.2021.3049547
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/access.2021.3049547
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.
Related Topics
Concepts
Feature selection
Pattern recognition (psychology)
Computer science
Artificial intelligence
Feature (linguistics)
FastICA
Binary number
Benchmark (surveying)
Principal component analysis
Cuckoo search
Feature vector
Swarm behaviour
Algorithm
Mathematics
Particle swarm optimization
Blind signal separation
Arithmetic
Philosophy
Geography
Channel (broadcasting)
Linguistics
Geodesy
Computer network
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2021.3049547
- https://ieeexplore.ieee.org/ielx7/6287639/9312710/09316226.pdf
- OA Status
- gold
- Cited By
- 80
- References
- 94
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3121034555
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3121034555Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/access.2021.3049547Digital Object Identifier
- Title
-
bSSA: Binary Salp Swarm Algorithm With Hybrid Data Transformation for Feature SelectionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-01-01Full publication date if available
- Authors
-
Sayar Singh Shekhawat, Harish Sharma, Sandeep Kumar, Anand Nayyar, Basit QureshiList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2021.3049547Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/6287639/9312710/09316226.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://ieeexplore.ieee.org/ielx7/6287639/9312710/09316226.pdfDirect OA link when available
- Concepts
-
Feature selection, Pattern recognition (psychology), Computer science, Artificial intelligence, Feature (linguistics), FastICA, Binary number, Benchmark (surveying), Principal component analysis, Cuckoo search, Feature vector, Swarm behaviour, Algorithm, Mathematics, Particle swarm optimization, Blind signal separation, Arithmetic, Philosophy, Geography, Channel (broadcasting), Linguistics, Geodesy, Computer networkTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
80Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 7, 2024: 15, 2023: 14, 2022: 27, 2021: 17Per-year citation counts (last 5 years)
- References (count)
-
94Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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