A hybrid binary dwarf mongoose optimization algorithm with simulated annealing for feature selection on high dimensional multi-class datasets
Abstract The dwarf mongoose optimization (DMO) algorithm developed in 2022 was applied to solve continuous mechanical engineering design problems with a considerable balance of the exploration and exploitation phases as a metaheuristic approach. Still, the DMO is restricted in its exploitation phase...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-09-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-18993-0 |
| _version_ | 1798032496768581632 |
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| author | Olatunji A. Akinola Absalom E. Ezugwu Olaide N. Oyelade Jeffrey O. Agushaka |
| author_facet | Olatunji A. Akinola Absalom E. Ezugwu Olaide N. Oyelade Jeffrey O. Agushaka |
| author_sort | Olatunji A. Akinola |
| collection | DOAJ |
| description | Abstract The dwarf mongoose optimization (DMO) algorithm developed in 2022 was applied to solve continuous mechanical engineering design problems with a considerable balance of the exploration and exploitation phases as a metaheuristic approach. Still, the DMO is restricted in its exploitation phase, somewhat hindering the algorithm's optimal performance. In this paper, we proposed a new hybrid method called the BDMSAO, which combines the binary variants of the DMO (or BDMO) and simulated annealing (SA) algorithm. In the modelling and implementation of the hybrid BDMSAO algorithm, the BDMO is employed and used as the global search method and the simulated annealing (SA) as the local search component to enhance the limited exploitative mechanism of the BDMO. The new hybrid algorithm was evaluated using eighteen (18) UCI machine learning datasets of low and medium dimensions. The BDMSAO was also tested using three high-dimensional medical datasets to assess its robustness. The results showed the efficacy of the BDMSAO in solving challenging feature selection problems on varying datasets dimensions and its outperformance over ten other methods in the study. Specifically, the BDMSAO achieved an overall result of 61.11% in producing the highest classification accuracy possible and getting 100% accuracy on 9 of 18 datasets. It also yielded the maximum accuracy obtainable on the three high-dimensional datasets utilized while achieving competitive performance regarding the number of features selected. |
| first_indexed | 2024-04-11T20:13:50Z |
| format | Article |
| id | doaj.art-8a5e5f6b983449cca33682132edf73ae |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-11T20:13:50Z |
| publishDate | 2022-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-8a5e5f6b983449cca33682132edf73ae2022-12-22T04:05:01ZengNature PortfolioScientific Reports2045-23222022-09-0112112210.1038/s41598-022-18993-0A hybrid binary dwarf mongoose optimization algorithm with simulated annealing for feature selection on high dimensional multi-class datasetsOlatunji A. Akinola0Absalom E. Ezugwu1Olaide N. Oyelade2Jeffrey O. Agushaka3School of Mathematics, Statistics, and Computer Science, University of KwaZulu-NatalSchool of Mathematics, Statistics, and Computer Science, University of KwaZulu-NatalSchool of Mathematics, Statistics, and Computer Science, University of KwaZulu-NatalSchool of Mathematics, Statistics, and Computer Science, University of KwaZulu-NatalAbstract The dwarf mongoose optimization (DMO) algorithm developed in 2022 was applied to solve continuous mechanical engineering design problems with a considerable balance of the exploration and exploitation phases as a metaheuristic approach. Still, the DMO is restricted in its exploitation phase, somewhat hindering the algorithm's optimal performance. In this paper, we proposed a new hybrid method called the BDMSAO, which combines the binary variants of the DMO (or BDMO) and simulated annealing (SA) algorithm. In the modelling and implementation of the hybrid BDMSAO algorithm, the BDMO is employed and used as the global search method and the simulated annealing (SA) as the local search component to enhance the limited exploitative mechanism of the BDMO. The new hybrid algorithm was evaluated using eighteen (18) UCI machine learning datasets of low and medium dimensions. The BDMSAO was also tested using three high-dimensional medical datasets to assess its robustness. The results showed the efficacy of the BDMSAO in solving challenging feature selection problems on varying datasets dimensions and its outperformance over ten other methods in the study. Specifically, the BDMSAO achieved an overall result of 61.11% in producing the highest classification accuracy possible and getting 100% accuracy on 9 of 18 datasets. It also yielded the maximum accuracy obtainable on the three high-dimensional datasets utilized while achieving competitive performance regarding the number of features selected.https://doi.org/10.1038/s41598-022-18993-0 |
| spellingShingle | Olatunji A. Akinola Absalom E. Ezugwu Olaide N. Oyelade Jeffrey O. Agushaka A hybrid binary dwarf mongoose optimization algorithm with simulated annealing for feature selection on high dimensional multi-class datasets Scientific Reports |
| title | A hybrid binary dwarf mongoose optimization algorithm with simulated annealing for feature selection on high dimensional multi-class datasets |
| title_full | A hybrid binary dwarf mongoose optimization algorithm with simulated annealing for feature selection on high dimensional multi-class datasets |
| title_fullStr | A hybrid binary dwarf mongoose optimization algorithm with simulated annealing for feature selection on high dimensional multi-class datasets |
| title_full_unstemmed | A hybrid binary dwarf mongoose optimization algorithm with simulated annealing for feature selection on high dimensional multi-class datasets |
| title_short | A hybrid binary dwarf mongoose optimization algorithm with simulated annealing for feature selection on high dimensional multi-class datasets |
| title_sort | hybrid binary dwarf mongoose optimization algorithm with simulated annealing for feature selection on high dimensional multi class datasets |
| url | https://doi.org/10.1038/s41598-022-18993-0 |
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