StackFeat-RL
Cross-source consensus on StackFeat-RL from 1 sources and 5 claims.
1 sources · 5 claims
Uses
How it works
Comparisons
Highlighted claims
- StackFeat-RL extends StackFeat by using a REINFORCE policy to learn feature-retention behavior and optional per-gene penalty modulation. — StackFeat RL: Reinforcement Learning over Iterative Dual Criterion Feature Selection for Stable Biomarker Discovery
- StackFeat-RL selects features through the intersection of top genes by accumulated coefficient magnitude and accumulated selection count. — StackFeat RL: Reinforcement Learning over Iterative Dual Criterion Feature Selection for Stable Biomarker Discovery
- StackFeat-RL uses one ElasticNetCV-selected alpha per outer training fold and holds it fixed during later selection episodes. — StackFeat RL: Reinforcement Learning over Iterative Dual Criterion Feature Selection for Stable Biomarker Discovery
- StackFeat-RL is presented as computationally much cheaper than base StackFeat while allowing independent episodes to be parallelized. — StackFeat RL: Reinforcement Learning over Iterative Dual Criterion Feature Selection for Stable Biomarker Discovery
- StackFeat-RL avoids manual specification of panel size, regularisation strength, and stopping criterion. — StackFeat RL: Reinforcement Learning over Iterative Dual Criterion Feature Selection for Stable Biomarker Discovery