Feature selection via a novel chaotic crow search algorithm

Crow search algorithm (CSA) is a new natural inspired algorithm proposed by Askarzadeh in 2016. The main inspiration of CSA came from crow search mechanism for hiding their food. Like most of the optimization algorithms, CSA suffers from low convergence rate and entrapment in local optima. In this p...

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Vydané v:Neural computing & applications Ročník 31; číslo 1; s. 171 - 188
Hlavní autori: Sayed, Gehad Ismail, Hassanien, Aboul Ella, Azar, Ahmad Taher
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Springer London 01.01.2019
Springer Nature B.V
Predmet:
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Entrapment
Heuristic methods
Image Processing and Computer Vision
Optimization
Optimization algorithms
Original Article
Probability and Statistics in Computer Science
Search algorithms
Feature selection
Optimization algorithm
Crow search algorithm
Chaos theory
ISSN:0941-0643, 1433-3058
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Popis
Shrnutí:Crow search algorithm (CSA) is a new natural inspired algorithm proposed by Askarzadeh in 2016. The main inspiration of CSA came from crow search mechanism for hiding their food. Like most of the optimization algorithms, CSA suffers from low convergence rate and entrapment in local optima. In this paper, a novel meta-heuristic optimizer, namely chaotic crow search algorithm (CCSA), is proposed to overcome these problems. The proposed CCSA is applied to optimize feature selection problem for 20 benchmark datasets. Ten chaotic maps are employed during the optimization process of CSA. The performance of CCSA is compared with other well-known and recent optimization algorithms. Experimental results reveal the capability of CCSA to find an optimal feature subset which maximizes the classification performance and minimizes the number of selected features. Moreover, the results show that CCSA is superior compared to CSA and the other algorithms. In addition, the experiments show that sine chaotic map is the appropriate map to significantly boost the performance of CSA.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-017-2988-6