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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Veröffentlicht in:Journal of bionics engineering Jg. 21; H. 4; S. 2000 - 2033
Hauptverfasser: Hammouri, Abdelaziz I., Awadallah, Mohammed A., Braik, Malik Sh, Al-Betar, Mohammed Azmi, Beseiso, Majdi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Singapore Springer Nature Singapore 01.07.2024
Springer Nature B.V
Schlagworte:
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
Feature selection
Optimization
Classification
Dwarf mongoose optimization algorithm
ISSN:1672-6529, 2543-2141
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Zusammenfassung: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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ISSN:1672-6529
2543-2141
DOI:10.1007/s42235-024-00524-4