Binary Horse Optimization Algorithm for Feature Selection
The bio-inspired research field has evolved greatly in the last few years due to the large number of novel proposed algorithms and their applications. The sources of inspiration for these novel bio-inspired algorithms are various, ranging from the behavior of groups of animals to the properties of v...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/15/5/156 |
| _version_ | 1797502163365134336 |
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| author | Dorin Moldovan |
| author_facet | Dorin Moldovan |
| author_sort | Dorin Moldovan |
| collection | DOAJ |
| description | The bio-inspired research field has evolved greatly in the last few years due to the large number of novel proposed algorithms and their applications. The sources of inspiration for these novel bio-inspired algorithms are various, ranging from the behavior of groups of animals to the properties of various plants. One problem is the lack of one bio-inspired algorithm which can produce the best global solution for all types of optimization problems. The presented solution considers the proposal of a novel approach for feature selection in classification problems, which is based on a binary version of a novel bio-inspired algorithm. The principal contributions of this article are: (1) the presentation of the main steps of the original Horse Optimization Algorithm (HOA), (2) the adaptation of the HOA to a binary version called the Binary Horse Optimization Algorithm (BHOA), (3) the application of the BHOA in feature selection using nine state-of-the-art datasets from the UCI machine learning repository and the classifiers Random Forest (RF), Support Vector Machines (SVM), Gradient Boosted Trees (GBT), Logistic Regression (LR), K-Nearest Neighbors (K-NN), and Naïve Bayes (NB), and (4) the comparison of the results with the ones obtained using the Binary Grey Wolf Optimizer (BGWO), Binary Particle Swarm Optimization (BPSO), and Binary Crow Search Algorithm (BCSA). The experiments show that the BHOA is effective and robust, as it returned the best mean accuracy value and the best accuracy value for four and seven datasets, respectively, compared to BGWO, BPSO, and BCSA, which returned the best mean accuracy value for four, two, and two datasets, respectively, and the best accuracy value for eight, seven, and five datasets, respectively. |
| first_indexed | 2024-03-10T03:29:00Z |
| format | Article |
| id | doaj.art-207e2b5e0f6a49358ee1f5c998651a7e |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-10T03:29:00Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-207e2b5e0f6a49358ee1f5c998651a7e2023-11-23T09:45:27ZengMDPI AGAlgorithms1999-48932022-05-0115515610.3390/a15050156Binary Horse Optimization Algorithm for Feature SelectionDorin Moldovan0Independent Researcher, 405200 Dej, RomaniaThe bio-inspired research field has evolved greatly in the last few years due to the large number of novel proposed algorithms and their applications. The sources of inspiration for these novel bio-inspired algorithms are various, ranging from the behavior of groups of animals to the properties of various plants. One problem is the lack of one bio-inspired algorithm which can produce the best global solution for all types of optimization problems. The presented solution considers the proposal of a novel approach for feature selection in classification problems, which is based on a binary version of a novel bio-inspired algorithm. The principal contributions of this article are: (1) the presentation of the main steps of the original Horse Optimization Algorithm (HOA), (2) the adaptation of the HOA to a binary version called the Binary Horse Optimization Algorithm (BHOA), (3) the application of the BHOA in feature selection using nine state-of-the-art datasets from the UCI machine learning repository and the classifiers Random Forest (RF), Support Vector Machines (SVM), Gradient Boosted Trees (GBT), Logistic Regression (LR), K-Nearest Neighbors (K-NN), and Naïve Bayes (NB), and (4) the comparison of the results with the ones obtained using the Binary Grey Wolf Optimizer (BGWO), Binary Particle Swarm Optimization (BPSO), and Binary Crow Search Algorithm (BCSA). The experiments show that the BHOA is effective and robust, as it returned the best mean accuracy value and the best accuracy value for four and seven datasets, respectively, compared to BGWO, BPSO, and BCSA, which returned the best mean accuracy value for four, two, and two datasets, respectively, and the best accuracy value for eight, seven, and five datasets, respectively.https://www.mdpi.com/1999-4893/15/5/156horse optimization algorithmfeature selectionclassificationmachine learningbio-inspired heuristicsnature inspired heuristics |
| spellingShingle | Dorin Moldovan Binary Horse Optimization Algorithm for Feature Selection Algorithms horse optimization algorithm feature selection classification machine learning bio-inspired heuristics nature inspired heuristics |
| title | Binary Horse Optimization Algorithm for Feature Selection |
| title_full | Binary Horse Optimization Algorithm for Feature Selection |
| title_fullStr | Binary Horse Optimization Algorithm for Feature Selection |
| title_full_unstemmed | Binary Horse Optimization Algorithm for Feature Selection |
| title_short | Binary Horse Optimization Algorithm for Feature Selection |
| title_sort | binary horse optimization algorithm for feature selection |
| topic | horse optimization algorithm feature selection classification machine learning bio-inspired heuristics nature inspired heuristics |
| url | https://www.mdpi.com/1999-4893/15/5/156 |
| work_keys_str_mv | AT dorinmoldovan binaryhorseoptimizationalgorithmforfeatureselection |