Ensemble of semi-supervised feature selection algorithms to reinforce heuristic function in ant colony optimization
Feature selection (FS) is a well-known dimensionality reduction method that chooses a hopeful subset of the original feature collection to diminish the influence the curse of dimensionality phenomenon. FS improves learning performance by removing irrelevant and redundant features. The significance o...
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Shahid Bahonar University of Kerman
2025-01-01
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| Series: | Journal of Mahani Mathematical Research |
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| Online Access: | https://jmmrc.uk.ac.ir/article_4480_ba6af5216f52d0a8fd0b2faaf1c68a8b.pdf |
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| author | Fereshteh Karimi Mohammad Bagher Dowlatshahi Amin Hashemi |
| author_facet | Fereshteh Karimi Mohammad Bagher Dowlatshahi Amin Hashemi |
| author_sort | Fereshteh Karimi |
| collection | DOAJ |
| description | Feature selection (FS) is a well-known dimensionality reduction method that chooses a hopeful subset of the original feature collection to diminish the influence the curse of dimensionality phenomenon. FS improves learning performance by removing irrelevant and redundant features. The significance of semi-supervised learning becomes obvious when labeled instances are not always accessible; however, labeling such data may be costly or time-consuming. Many of the samples in semi-supervised learning are unlabeled. Semi-supervised FS techniques overcome this problem, simultaneously utilizing information from labeled and unlabeled data. This article presents a new semi-supervised FS method called ESACO. ESACO uses a combination of ACO algorithm and a set of heuristics to select the best features. Ant colony optimization algorithm (ACO) is a metaheuristic method for solving optimization problems. Heuristic selection is a significant part of the ACO algorithm that can influence the movements of ants. Utilizing numerous heuristics rather than a single one can improve the performance of the ACO algorithm. However, using multiple heuristics investigates other aspects to attain optimal and better solutions in ACO and provides us with more information. Thus, in the ESACO, we have utilized the ensemble of heuristic functions by integrating them into Multi-Criteria Decision-Making (MCDM) procedure. So far, the utilization of multiple heuristics in ACO has not been studied in semi-supervised FS. We have compared the performance of the ESACO using the KNN classifier with variant experiments with eight semi-supervised FS techniques and 15 datasets. Considering the obtained results, the efficiency of the presented method is significantly better than the competing methods. The article's code link on GitHub can also be found at the following: https://github.com/frshkara/ESACO. |
| format | Article |
| id | doaj-art-c4ad390230cf49e3a922aba03c3870a1 |
| institution | Kabale University |
| issn | 2251-7952 2645-4505 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Shahid Bahonar University of Kerman |
| record_format | Article |
| series | Journal of Mahani Mathematical Research |
| spelling | doaj-art-c4ad390230cf49e3a922aba03c3870a12025-01-04T19:30:18ZengShahid Bahonar University of KermanJournal of Mahani Mathematical Research2251-79522645-45052025-01-0114128332610.22103/jmmr.2024.23194.16074480Ensemble of semi-supervised feature selection algorithms to reinforce heuristic function in ant colony optimizationFereshteh Karimi0Mohammad Bagher Dowlatshahi1Amin Hashemi2Department of Computer Engineering, Lorestan University, Khoramabad, IranDepartment of Computer Engineering, Lorestan University, Khoramabad, IranDepartment of Computer Engineering, Lorestan University, Khoramabad, IranFeature selection (FS) is a well-known dimensionality reduction method that chooses a hopeful subset of the original feature collection to diminish the influence the curse of dimensionality phenomenon. FS improves learning performance by removing irrelevant and redundant features. The significance of semi-supervised learning becomes obvious when labeled instances are not always accessible; however, labeling such data may be costly or time-consuming. Many of the samples in semi-supervised learning are unlabeled. Semi-supervised FS techniques overcome this problem, simultaneously utilizing information from labeled and unlabeled data. This article presents a new semi-supervised FS method called ESACO. ESACO uses a combination of ACO algorithm and a set of heuristics to select the best features. Ant colony optimization algorithm (ACO) is a metaheuristic method for solving optimization problems. Heuristic selection is a significant part of the ACO algorithm that can influence the movements of ants. Utilizing numerous heuristics rather than a single one can improve the performance of the ACO algorithm. However, using multiple heuristics investigates other aspects to attain optimal and better solutions in ACO and provides us with more information. Thus, in the ESACO, we have utilized the ensemble of heuristic functions by integrating them into Multi-Criteria Decision-Making (MCDM) procedure. So far, the utilization of multiple heuristics in ACO has not been studied in semi-supervised FS. We have compared the performance of the ESACO using the KNN classifier with variant experiments with eight semi-supervised FS techniques and 15 datasets. Considering the obtained results, the efficiency of the presented method is significantly better than the competing methods. The article's code link on GitHub can also be found at the following: https://github.com/frshkara/ESACO.https://jmmrc.uk.ac.ir/article_4480_ba6af5216f52d0a8fd0b2faaf1c68a8b.pdfant colony optimizationensemble of heuristicssemi-supervised learningensemble feature selectionmulti-criteria decision-making |
| spellingShingle | Fereshteh Karimi Mohammad Bagher Dowlatshahi Amin Hashemi Ensemble of semi-supervised feature selection algorithms to reinforce heuristic function in ant colony optimization Journal of Mahani Mathematical Research ant colony optimization ensemble of heuristics semi-supervised learning ensemble feature selection multi-criteria decision-making |
| title | Ensemble of semi-supervised feature selection algorithms to reinforce heuristic function in ant colony optimization |
| title_full | Ensemble of semi-supervised feature selection algorithms to reinforce heuristic function in ant colony optimization |
| title_fullStr | Ensemble of semi-supervised feature selection algorithms to reinforce heuristic function in ant colony optimization |
| title_full_unstemmed | Ensemble of semi-supervised feature selection algorithms to reinforce heuristic function in ant colony optimization |
| title_short | Ensemble of semi-supervised feature selection algorithms to reinforce heuristic function in ant colony optimization |
| title_sort | ensemble of semi supervised feature selection algorithms to reinforce heuristic function in ant colony optimization |
| topic | ant colony optimization ensemble of heuristics semi-supervised learning ensemble feature selection multi-criteria decision-making |
| url | https://jmmrc.uk.ac.ir/article_4480_ba6af5216f52d0a8fd0b2faaf1c68a8b.pdf |
| work_keys_str_mv | AT fereshtehkarimi ensembleofsemisupervisedfeatureselectionalgorithmstoreinforceheuristicfunctioninantcolonyoptimization AT mohammadbagherdowlatshahi ensembleofsemisupervisedfeatureselectionalgorithmstoreinforceheuristicfunctioninantcolonyoptimization AT aminhashemi ensembleofsemisupervisedfeatureselectionalgorithmstoreinforceheuristicfunctioninantcolonyoptimization |