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On COVID-19 miRNA data (GSE240888, 332 features) and three Alzheimer's disease classification tasks (GSE84422, 13237 genes; Normal vs.\ Possible, Probable, and Definite AD), StackFeat-RL achieves the highest predictive accuracy among all evaluated methods, including ElasticNet, Boruta, mRMR, and stability selection, while requiring 3--4$\times$ fewer features.
Keywords: feature selection, reinforcement learning, REINFORCE, elastic net, biomarker discovery, Alzheimer's disease, dual-criterion selection, protein interaction networks
| Comments: | 7 pages. Submitted to eccb2026 |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2604.22892 [cs.LG] |
| (or arXiv:2604.22892v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2604.22892 arXiv-issued DOI via DataCite |
From: David A. Herrera-Martí [view email]
[v1]
Fri, 24 Apr 2026 09:52:33 UTC (483 KB)
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