Improved Dwarf Mongoose Optimization Algorithm for Feature Selection: Application in Software Fault Prediction Datasets
Feature selection (FS) plays a crucial role in pre-processing machine learning datasets, as it eliminates redundant features to improve classification accuracy and reduce computational costs. This paper presents an enhanced approach to FS for software fault prediction, specifically by enhancing the...
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| Published in | Journal of bionics engineering Vol. 21; no. 4; pp. 2000 - 2033 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Singapore
Springer Nature Singapore
01.07.2024
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1672-6529 2543-2141 |
| DOI | 10.1007/s42235-024-00524-4 |
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| Abstract | Feature selection (FS) plays a crucial role in pre-processing machine learning datasets, as it eliminates redundant features to improve classification accuracy and reduce computational costs. This paper presents an enhanced approach to FS for software fault prediction, specifically by enhancing the binary dwarf mongoose optimization (BDMO) algorithm with a crossover mechanism and a modified positioning updating formula. The proposed approach, termed iBDMOcr, aims to fortify exploration capability, promote population diversity, and lastly improve the wrapper-based FS process for software fault prediction tasks. iBDMOcr gained superb performance compared to other well-esteemed optimization methods across 17 benchmark datasets. It ranked first in 11 out of 17 datasets in terms of average classification accuracy. Moreover, iBDMOcr outperformed other methods in terms of average fitness values and number of selected features across all datasets. The findings demonstrate the effectiveness of iBDMOcr in addressing FS problems in software fault prediction, leading to more accurate and efficient models. |
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| AbstractList | Feature selection (FS) plays a crucial role in pre-processing machine learning datasets, as it eliminates redundant features to improve classification accuracy and reduce computational costs. This paper presents an enhanced approach to FS for software fault prediction, specifically by enhancing the binary dwarf mongoose optimization (BDMO) algorithm with a crossover mechanism and a modified positioning updating formula. The proposed approach, termed iBDMOcr, aims to fortify exploration capability, promote population diversity, and lastly improve the wrapper-based FS process for software fault prediction tasks. iBDMOcr gained superb performance compared to other well-esteemed optimization methods across 17 benchmark datasets. It ranked first in 11 out of 17 datasets in terms of average classification accuracy. Moreover, iBDMOcr outperformed other methods in terms of average fitness values and number of selected features across all datasets. The findings demonstrate the effectiveness of iBDMOcr in addressing FS problems in software fault prediction, leading to more accurate and efficient models. |
| Author | Al-Betar, Mohammed Azmi Beseiso, Majdi Braik, Malik Sh Awadallah, Mohammed A. Hammouri, Abdelaziz I. |
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| Copyright | Jilin University 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Jilin University 2024. |
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| SubjectTerms | Accuracy Algorithms Artificial Intelligence Biochemical Engineering Bioinformatics Biomaterials Biomedical Engineering and Bioengineering Biomedical Engineering/Biotechnology Classification Datasets Engineering Fault diagnosis Machine learning Optimization Predictions Research Article Software |
| Title | Improved Dwarf Mongoose Optimization Algorithm for Feature Selection: Application in Software Fault Prediction Datasets |
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