Global chaotic bat algorithm for feature selection

The wrapper algorithm adopts the performance of the learning algorithm as the evaluation criteria to obtain excellent classification performance. However, the wrapper algorithm is prone to converge prematurely. A global chaotic bat algorithm (GCBA) is put up forward to improve this shortage. First,...

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Vydáno v:The Journal of supercomputing Ročník 78; číslo 17; s. 18754 - 18776
Hlavní autoři: Li, Ying, Cui, Xueting, Fan, Jiahao, Wang, Tan
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.11.2022
Springer Nature B.V
Témata:
Adaptive learning
Algorithms
Classification
Compilers
Computer Science
Datasets
Gene expression
Interpreters
Machine learning
Processor Architectures
Programming Languages
Solution space
Transfer functions
Wrapper feature selection algorithm
Feature selection
Bat algorithm
Chaotic map
Classification
ISSN:0920-8542, 1573-0484
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Shrnutí:The wrapper algorithm adopts the performance of the learning algorithm as the evaluation criteria to obtain excellent classification performance. However, the wrapper algorithm is prone to converge prematurely. A global chaotic bat algorithm (GCBA) is put up forward to improve this shortage. First, GCBA applies chaotic map to population initialization to cover the entire solution space. In addition, adaptive learning factors are presented to balance exploration and exploration. The learning factor of local optimal position gradually decreases in the early stage while the learning factor of global optimal position gradually increases in the later stage. Finally, to improve the exploitation, an improved transfer function is proposed, which transfers the continuous space to discrete binary space. GCBA is tested on 14 UCI data sets and 5 gene expression data sets compared with other 6 comparison algorithms. Compared with other algorithms, the results show that GCBA is able to achieve better classification performance.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-022-04606-0