A multi-objective optimization algorithm for feature selection problems

Feature selection (FS) is a critical step in data mining, and machine learning algorithms play a crucial role in algorithms performance. It reduces the processing time and accuracy of the categories. In this paper, three different solutions are proposed to FS. In the first solution, the Harris Hawks...

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Published in:	Engineering with computers Vol. 38; no. Suppl 3; pp. 1845 - 1863
Main Authors:	Abdollahzadeh, Benyamin, Gharehchopogh, Farhad Soleimanian
Format:	Journal Article
Language:	English
Published:	London Springer London 01.08.2022 Springer Nature B.V
Subjects:	Algorithms CAE) and Design Calculus of Variations and Optimal Control; Optimization Classical Mechanics Computer Science Computer-Aided Engineering (CAD Control Control methods Data mining Datasets Feature selection Machine learning Math. Applications in Chemistry Mathematical and Computational Engineering Multiple objective analysis Optimization algorithms Original Article Pareto optimization Standard data Statistical tests Systems Theory Multiobjective Feature selection Bonferroni–Holm Fruitfly optimization algorithm Family-wise error rate Harris hawks optimization
ISSN:	0177-0667, 1435-5663
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Abstract	Feature selection (FS) is a critical step in data mining, and machine learning algorithms play a crucial role in algorithms performance. It reduces the processing time and accuracy of the categories. In this paper, three different solutions are proposed to FS. In the first solution, the Harris Hawks Optimization (HHO) algorithm has been multiplied, and in the second solution, the Fruitfly Optimization Algorithm (FOA) has been multiplied, and in the third solution, these two solutions are hydride and are named MOHHOFOA. The results were tested with MOPSO, NSGA-II, BGWOPSOFS and B-MOABC algorithms for FS on 15 standard data sets with mean, best, worst, standard deviation (STD) criteria. The Wilcoxon statistical test was also used with a significance level of 5% and the Bonferroni–Holm method to control the family-wise error rate. The results are shown in the Pareto front charts, indicating that the proposed solutions' performance on the data set is promising.
AbstractList	Feature selection (FS) is a critical step in data mining, and machine learning algorithms play a crucial role in algorithms performance. It reduces the processing time and accuracy of the categories. In this paper, three different solutions are proposed to FS. In the first solution, the Harris Hawks Optimization (HHO) algorithm has been multiplied, and in the second solution, the Fruitfly Optimization Algorithm (FOA) has been multiplied, and in the third solution, these two solutions are hydride and are named MOHHOFOA. The results were tested with MOPSO, NSGA-II, BGWOPSOFS and B-MOABC algorithms for FS on 15 standard data sets with mean, best, worst, standard deviation (STD) criteria. The Wilcoxon statistical test was also used with a significance level of 5% and the Bonferroni–Holm method to control the family-wise error rate. The results are shown in the Pareto front charts, indicating that the proposed solutions' performance on the data set is promising.
Author	Gharehchopogh, Farhad Soleimanian Abdollahzadeh, Benyamin
Author_xml	– sequence: 1 givenname: Benyamin surname: Abdollahzadeh fullname: Abdollahzadeh, Benyamin organization: Department of Computer Engineering, Urmia Branch, Islamic Azad University – sequence: 2 givenname: Farhad Soleimanian orcidid: 0000-0003-1588-1659 surname: Gharehchopogh fullname: Gharehchopogh, Farhad Soleimanian email: bonab.farhad@gmail.com organization: Department of Computer Engineering, Urmia Branch, Islamic Azad University
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Tehnicki vjesnik – ident: 1369_CR17 – ident: 1369_CR5 doi: 10.1002/int.22342
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SubjectTerms	Algorithms CAE) and Design Calculus of Variations and Optimal Control; Optimization Classical Mechanics Computer Science Computer-Aided Engineering (CAD Control Control methods Data mining Datasets Feature selection Machine learning Math. Applications in Chemistry Mathematical and Computational Engineering Multiple objective analysis Optimization algorithms Original Article Pareto optimization Standard data Statistical tests Systems Theory
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