Paper Info
Title
Feature selection strategy based on hybrid crow search optimization algorithm integrated with chaos theory and fuzzy c-means algorithm for medical diagnosis problems
Abstract
Powerful knowledge acquisition tools and techniques have the ability to increase both the quality and the quantity of knowledge-based systems for real-world problems. In this paper, we designed a hybrid crow search optimization algorithm integrated with chaos theory and fuzzy c-means algorithm denoted as CFCSA for feature selection problems of medical diagnosis. In the proposed CFCSA framework, the crow search algorithm adopts the global optimization technique to avoid the sensitivity of local optimization. The fuzzy c-means (FCM) objective function is used as a cost function for the chaotic crow search optimization algorithm. The proposed algorithm CFCSA is benchmarked against the binary crow search algorithm (BCSA), chaotic ant lion optimization algorithm (CALO), binary ant lion optimization algorithm (BALO) and bat algorithm relevant methods. The proposed CFCSA algorithm vs. BCSA, CALO, BALO and bat algorithm is tested on diabetes, heart, Radiopaedia CT liver, breast cancer, lung cancer, cardiotocography, ILPD, liver disorders, hepatitis and arrhythmia. Experimental results show the proposed method CFCSA is better against comparative models in feature selection on the medical diagnosis data sets.
Year
DOI
Venue
2020
10.1007/s00500-019-03988-3
Soft Computing
Keywords
Field
DocType
Feature selection, Crow search optimization, Chaos theory, Fuzzy c-means, Medical diagnosis
Bat algorithm,Feature selection,Global optimization,Computer science,Fuzzy logic,Algorithm,Local search (optimization),Chaotic,Knowledge acquisition,Medical diagnosis
Journal
Volume
Issue
ISSN
24
3
1432-7643
Citations 
PageRank 
References 
8
0.43
0
Authors
2
Name
Order
Citations
PageRank
Ahmed M. Anter1447.37
Mumtaz Ali217112.30