Artificial rabbits optimization algorithm with automatically DBSCAN clustering algorithm to similarity agent update for features selection problems
Feature selection is one of the important steps in data mining to reduce the dimensions of datasets. Due to the fact that feature selection is inherently a NP-hard problem, no deterministic algorithm has been identified to solve this problem in acceptable time. Meta-heuristic algorithms are reliable...
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| Veröffentlicht in: | The Journal of supercomputing Jg. 81; H. 1; S. 150 |
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| Sprache: | Englisch |
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01.01.2025
Springer Nature B.V |
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| ISSN: | 0920-8542, 1573-0484 |
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| Abstract | Feature selection is one of the important steps in data mining to reduce the dimensions of datasets. Due to the fact that feature selection is inherently a NP-hard problem, no deterministic algorithm has been identified to solve this problem in acceptable time. Meta-heuristic algorithms are reliable alternatives to solve such problems in acceptable time. In the literature, a large number of algorithms have been proposed to solve the feature selection problem using meta-heuristic optimization algorithms. In this work, a new feature selection algorithm based on ARO meta-heuristic algorithm and DBSCAN clustering algorithm with automatic adjustment of input parameters (ARO-DBSCAN) is proposed. Using side algorithms to improve the performance of meta-heuristic algorithms can potentially cause getting stuck in local optima. The method proposed in the work has improved the performance of the ARO meta-heuristic for the feature selection problem without increasing the probability stuck in local optima. The use of DBSCAN clustering algorithm, which is based on density, increases the exploitation of ARO in the search space while maintaining its exploration. As a result, the performance of the ARO algorithm increases significantly in feature selection problems. The proposed algorithm is compared with 8 state-of-the-art feature selection algorithms on the UCI benchmark datasets and three real-world high-dimensional datasets. Result of experiments show the better performance of ARO-DBSCAN algorithm in the appropriate execution time. Also, in high-dimensional data, the proposed method is able to significantly reduce the number of dataset features. which makes the analysis of these datasets more efficient. The source code for the algorithm being proposed is accessible to the public on
https://github.com/alihamdipour/ARO-DBSCAN
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| AbstractList | Feature selection is one of the important steps in data mining to reduce the dimensions of datasets. Due to the fact that feature selection is inherently a NP-hard problem, no deterministic algorithm has been identified to solve this problem in acceptable time. Meta-heuristic algorithms are reliable alternatives to solve such problems in acceptable time. In the literature, a large number of algorithms have been proposed to solve the feature selection problem using meta-heuristic optimization algorithms. In this work, a new feature selection algorithm based on ARO meta-heuristic algorithm and DBSCAN clustering algorithm with automatic adjustment of input parameters (ARO-DBSCAN) is proposed. Using side algorithms to improve the performance of meta-heuristic algorithms can potentially cause getting stuck in local optima. The method proposed in the work has improved the performance of the ARO meta-heuristic for the feature selection problem without increasing the probability stuck in local optima. The use of DBSCAN clustering algorithm, which is based on density, increases the exploitation of ARO in the search space while maintaining its exploration. As a result, the performance of the ARO algorithm increases significantly in feature selection problems. The proposed algorithm is compared with 8 state-of-the-art feature selection algorithms on the UCI benchmark datasets and three real-world high-dimensional datasets. Result of experiments show the better performance of ARO-DBSCAN algorithm in the appropriate execution time. Also, in high-dimensional data, the proposed method is able to significantly reduce the number of dataset features. which makes the analysis of these datasets more efficient. The source code for the algorithm being proposed is accessible to the public on https://github.com/alihamdipour/ARO-DBSCAN. Feature selection is one of the important steps in data mining to reduce the dimensions of datasets. Due to the fact that feature selection is inherently a NP-hard problem, no deterministic algorithm has been identified to solve this problem in acceptable time. Meta-heuristic algorithms are reliable alternatives to solve such problems in acceptable time. In the literature, a large number of algorithms have been proposed to solve the feature selection problem using meta-heuristic optimization algorithms. In this work, a new feature selection algorithm based on ARO meta-heuristic algorithm and DBSCAN clustering algorithm with automatic adjustment of input parameters (ARO-DBSCAN) is proposed. Using side algorithms to improve the performance of meta-heuristic algorithms can potentially cause getting stuck in local optima. The method proposed in the work has improved the performance of the ARO meta-heuristic for the feature selection problem without increasing the probability stuck in local optima. The use of DBSCAN clustering algorithm, which is based on density, increases the exploitation of ARO in the search space while maintaining its exploration. As a result, the performance of the ARO algorithm increases significantly in feature selection problems. The proposed algorithm is compared with 8 state-of-the-art feature selection algorithms on the UCI benchmark datasets and three real-world high-dimensional datasets. Result of experiments show the better performance of ARO-DBSCAN algorithm in the appropriate execution time. Also, in high-dimensional data, the proposed method is able to significantly reduce the number of dataset features. which makes the analysis of these datasets more efficient. The source code for the algorithm being proposed is accessible to the public on https://github.com/alihamdipour/ARO-DBSCAN . |
| ArticleNumber | 150 |
| Author | Basiri, Abdolali Hamdipour, Ali Zaare, Mostafa Mirjalili, Seyedali |
| Author_xml | – sequence: 1 givenname: Ali surname: Hamdipour fullname: Hamdipour, Ali organization: Department of Mathematics and Computer Sciences, Damghan University – sequence: 2 givenname: Abdolali surname: Basiri fullname: Basiri, Abdolali email: basiri@du.ac.ir organization: Department of Mathematics and Computer Sciences, Damghan University – sequence: 3 givenname: Mostafa surname: Zaare fullname: Zaare, Mostafa organization: Department of Mathematics and Computer Sciences, Damghan University – sequence: 4 givenname: Seyedali surname: Mirjalili fullname: Mirjalili, Seyedali organization: Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Faculty of Electrical Engineering and Computer Science, VŠB-TU Ostrava |
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| Cites_doi | 10.1145/304181.304187 10.1109/JSTARS.2012.2185822 10.1016/j.asoc.2023.110031 10.1016/j.eswa.2019.05.035 10.1016/j.knosys.2021.107283 10.1016/j.asoc.2020.106794 10.1016/j.cose.2017.06.005 10.1016/j.ygeno.2011.04.011 10.1016/j.eswa.2008.08.022 10.1016/j.engappai.2022.105082 10.1016/j.knosys.2019.105190 10.1177/003754970107600201 10.1007/s10489-021-03118-3 10.1109/ACCESS.2023.3298955 10.1145/3068335 10.1016/j.advengsoft.2013.03.004 10.1016/j.asoc.2022.109464 10.1016/j.advengsoft.2013.12.007 10.1016/j.advengsoft.2016.01.008 10.1016/j.cie.2020.106559 10.1016/j.asoc.2015.10.034 10.1109/ACCESS.2019.2931334 10.1016/j.knosys.2015.12.022 10.1007/s00521-019-04159-z 10.1016/j.knosys.2011.07.001 10.1016/j.eswa.2013.09.004 10.1016/j.eswa.2022.119130 10.1016/j.neucom.2022.04.083 10.1016/j.swevo.2018.02.013 10.1016/S0004-3702(97)00043-X 10.1007/s00521-011-0632-4 10.1002/int.22535 10.1016/j.patrec.2006.09.003 10.1016/j.neucom.2017.04.053 10.1109/ACCESS.2020.2999093 10.1016/j.engappai.2017.01.006 10.3389/fenrg.2021.652801 10.1016/j.asoc.2017.11.006 10.1016/j.asoc.2015.03.003 10.1016/j.eswa.2006.04.010 10.1007/s12559-019-09668-6 10.1109/ACCESS.2020.2996611 10.1109/ICCCCM.2016.7918233 10.1214/ss/1177011077 10.1109/MHS.1995.494215 10.1109/ICSSSM.2007.4280175 10.1109/ACCESS.2024.3438104 10.1007/978-3-642-32894-7_27 10.1109/CEC.1999.782657 10.1109/ICADIWT.2014.6814687 10.1109/ACCESS.2023.3312022 10.1007/s13369-024-09222-z |
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| Keywords | DBSCAN Feature selection Artificial rabbits optimization ARO Optimization |
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| Title | Artificial rabbits optimization algorithm with automatically DBSCAN clustering algorithm to similarity agent update for features selection problems |
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