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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 14/2022

27.07.2020 | S.I. : Healthcare Analytics

The monarch butterfly optimization algorithm for solving feature selection problems

verfasst von: Mohammed Alweshah, Saleh Al Khalaileh, Brij B. Gupta, Ammar Almomani, Abdelaziz I. Hammouri, Mohammed Azmi Al-Betar

Erschienen in: Neural Computing and Applications | Ausgabe 14/2022

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Abstract

Feature selection (FS) is considered to be a hard optimization problem in data mining and some artificial intelligence fields. It is a process where rather than studying all of the features of a whole dataset, some associated features of a problem are selected, the aim of which is to increase classification accuracy and reduce computational time. In this paper, a recent optimization algorithm, the monarch butterfly optimization (MBO) algorithm, is implemented with a wrapper FS method that uses the k-nearest neighbor (KNN) classifier. Experiments were implemented on 18 benchmark datasets. The results showed that, in comparison with four metaheuristic algorithms (WOASAT, ALO, GA and PSO), MBO was superior, giving a high rate of classification accuracy of, on average, 93% for all datasets as well as reducing the selection size significantly. Therefore, the use of the MBO to solve the FS problems has been proven through the results obtained to be effective and highly efficient in this field, and the results have also proven the strength of the balance between global and local search of MBO.
Vorheriger Artikel An IoT-based smart healthcare system to detect dysphonia
Nächster Artikel A deep learning system for health care IoT and smartphone malware detection

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Metadaten
Titel
The monarch butterfly optimization algorithm for solving feature selection problems
verfasst von
Mohammed Alweshah
Saleh Al Khalaileh
Brij B. Gupta
Ammar Almomani
Abdelaziz I. Hammouri
Mohammed Azmi Al-Betar
Publikationsdatum
27.07.2020
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 14/2022
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-05210-0

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