Journal of System Simulation
Abstract
Abstract: Traditional feature selection approaches have major limitations for high-dimensional microarrays, and it is difficult to accurately and efficiently propose the best feature subset. To address this problem, a multi-strategy hybrid artificial bee colony (ABC) algorithm based on wrapper is proposed, which mixes chaotic opposition-based learning strategy, elite guidance strategy, and Mantegna Lévy distribution strategy, and proposes two new search strategies in the employed and onlooker bee phases respectively. A new objective function is proposed for the microarray high-dimensional feature selection problem, which balances the optimal performance of the model with the minimization of the feature subset size. Experimental results show that the algorithm is able to achieve high classification accuracy while still satisfying the feature subset size minimization objective to some extent. Moreover, it outperforms improved algorithms such as GABC and six new intelligent algorithms such as the salp swarm algorithm.
Recommended Citation
Qin, Chuandong; Li, Baosheng; and Han, Baole
(2023)
"Multi-strategy Hybrid ABC for Microarray High-Dimensional Feature Selection,"
Journal of System Simulation: Vol. 35:
Iss.
3, Article 6.
DOI: 10.16182/j.issn1004731x.joss.21-1188
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol35/iss3/6
First Page
515
Revised Date
2022-01-06
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.21-1188
Last Page
524
CLC
TP181
Recommended Citation
Chuandong Qin, Baosheng Li, Baole Han. Multi-strategy Hybrid ABC for Microarray High-Dimensional Feature Selection[J]. Journal of System Simulation, 2023, 35(3): 515-524.
DOI
10.16182/j.issn1004731x.joss.21-1188
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