Paper Info
Title
The monarch butterfly optimization algorithm for solving feature selection problems
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.
Year
DOI
Venue
2022
10.1007/S00521-020-05210-0
Neural Computing and Applications
DocType
Volume
Citations 
Journal
34
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Mohammed Alweshah100.34
Saleh Al Khalaileh200.34
B. B. Gupta351846.49
Ammar Almomani41168.68
Abdelaziz I. Hammouri5836.51
Mohammed Azmi Al-Betar662043.69