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Publication
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| Title of Article |
An Asymmetric Chaotic Competitive Swarm Optimization Algorithm for Feature Selection in High-Dimensional Data |
| Date of Acceptance |
22 October 2020 |
| Journal |
| Title of Journal |
symmetry |
| Standard |
ISI |
| Institute of Journal |
MDPI (Basel, Switzerland) |
| ISBN/ISSN |
27 |
| Volume |
2020 |
| Issue |
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| Month |
์November |
| Year of Publication |
2021 |
| Page |
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| Abstract |
This paper presents a method for feature selection in a high-dimensional classification
context. The proposed method finds a candidate solution based on quality criteria using subset
searching. In this study, the competitive swarm optimization (CSO) algorithm was implemented to
solve feature selection problems in high-dimensional data. A new asymmetric chaotic function was
proposed and used to generate the population and search for a CSO solution. Its histogram
is right-skewed. The proposed method is named an asymmetric chaotic competitive swarm
optimization algorithm (ACCSO). According to the asymmetrical property of the proposed chaotic
map, ACCSO prefers zero than one. Therefore, the solution is very compact and can achieve high
classification accuracy with a minimal feature subset for high-dimensional datasets. The proposed
method was evaluated on 12 datasets, with dimensions ranging from 4 to 10,304. ACCSO was
compared to the original CSO algorithm and other metaheuristic algorithms. Experimental results
show that the proposed method can increase accuracy and it reduces the number of selected features.
Compared to different optimization algorithms with other wrappers, the proposed method exhibits
excellent performance.
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| Keyword |
asymmetry; chaos; skewed distribution; competitive swarm optimizer; metaheuristic algorithm; high-dimensional; feature selection |
| Author |
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| Reviewing Status |
มีผู้ประเมินอิสระ |
| Status |
ตีพิมพ์แล้ว |
| Level of Publication |
นานาชาติ |
| citation |
false |
| Part of thesis |
true |
| Attach file |
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| Citation |
0
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Journal Publication
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