Irfan Khan1, Xianchao Zhang1,*, Ramesh Kumar Ayyasamy2,*, Saadat M. Alhashmi3, Azizur Rahim4
CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1611-1636, 2025, DOI:10.32604/cmes.2025.058566
- 27 January 2025
Abstract The rapid growth of machine learning (ML) across fields has intensified the challenge of selecting the right algorithm for specific tasks, known as the Algorithm Selection Problem (ASP). Traditional trial-and-error methods have become impractical due to their resource demands. Automated Machine Learning (AutoML) systems automate this process, but often neglect the group structures and sparsity in meta-features, leading to inefficiencies in algorithm recommendations for classification tasks. This paper proposes a meta-learning approach using Multivariate Sparse Group Lasso (MSGL) to address these limitations. Our method models both within-group and across-group sparsity among meta-features to manage high-dimensional More >
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