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
Online-Zugang:	Volltext
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