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Soft Computing
Erschienen in: Soft Computing 3/2020

20.04.2019 | Methodologies and Application

Feature selection strategy based on hybrid crow search optimization algorithm integrated with chaos theory and fuzzy c-means algorithm for medical diagnosis problems

verfasst von: Ahmed M. Anter, Mumtaz Ali

Erschienen in: Soft Computing | Ausgabe 3/2020

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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.
Vorheriger Artikel Fuzzy-based approach to assess and prioritize privacy risks
Nächster Artikel Pythagorean uncertain linguistic hesitant fuzzy weighted averaging operator and its application in financial group decision making

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Metadaten
Titel
Feature selection strategy based on hybrid crow search optimization algorithm integrated with chaos theory and fuzzy c-means algorithm for medical diagnosis problems
verfasst von
Ahmed M. Anter
Mumtaz Ali
Publikationsdatum
20.04.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 3/2020
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-019-03988-3

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