@Article{cmes.2025.058566,
AUTHOR = {Irfan Khan, Xianchao Zhang, Ramesh Kumar Ayyasamy, Saadat M. Alhashmi, Azizur Rahim},
TITLE = {Enhancing Classification Algorithm Recommendation in Automated Machine Learning: A Meta-Learning Approach Using Multivariate Sparse Group Lasso},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {142},
YEAR = {2025},
NUMBER = {2},
PAGES = {1611--1636},
URL = {http://www.techscience.com/CMES/v142n2/59372},
ISSN = {1526-1506},
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 data and reduce multicollinearity across eight meta-feature groups. The Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) with adaptive restart efficiently solves the non-smooth optimization problem. Empirical validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches, achieving 77.18% classification accuracy, 86.07% recommendation accuracy and 88.83% normalized discounted cumulative gain.},
DOI = {10.32604/cmes.2025.058566}
}