StackFeat RL: Reinforcement Learning over Iterative Dual Criterion Feature Selection for Stable Biomarker Discovery

TL;DR

StackFeat-RL is a reinforcement learning framework that optimizes iterative dual-criterion feature selection, leading to more accurate, sparse, and stable biomarker discovery in high-dimensional genomic data.

Contribution

It introduces a novel meta-learning approach using REINFORCE to optimize dual-criterion feature selection, improving stability and accuracy over existing methods.

Findings

Achieves highest predictive accuracy on COVID-19 and Alzheimer's datasets.

Requires 3-4 times fewer features than competing methods.

Provides convergence guarantees through iterative accumulation.

Abstract

Feature selection in high-dimensional genomic data ($d \gg n$) demands methods that are simultaneously accurate, sparse, and stable. Existing approaches either require manual threshold specification (mRMR, stability selection), produce unstable selections under data perturbation (Lasso, Boruta), or ignore biological structure entirely. We introduce StackFeat-RL, a meta-learning framework that optimises the hyperparameters of an iterative dual-criterion feature selection algorithm via REINFORCE policy gradients. The dual criterion, requiring both coefficient consistency and selection frequency, guards against two failure modes missed by single-criterion methods, while iterative accumulation provides convergence guarantees via the law of large numbers. 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