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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| Vydané v: | Engineering with computers Ročník 38; číslo Suppl 3; s. 1845 - 1863 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
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London
Springer London
01.08.2022
Springer Nature B.V |
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| 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. |
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| 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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| Keywords | Multiobjective Feature selection Bonferroni–Holm Fruitfly optimization algorithm Family-wise error rate Harris hawks optimization |
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