Papers › Developing parsimonious ensembles using predictor diversity within a reinforcement...

Developing parsimonious ensembles using predictor diversity within a reinforcement learning framework

15 Feb 2021arXiv:2102.07344archive 2025-07-28

Ana Stanescu, Gaurav Pandey

Heterogeneous ensembles that can aggregate an unrestricted number and variety of base predictors can effectively address challenging prediction problems. In particular, accurate ensembles that are also parsimonious, i.e., consist of as few base predictors as possible, can help reveal potentially useful knowledge about the target problem domain. Although ensemble selection offers a potential approach to achieving these goals, the currently available algorithms are limited in their abilities. In this paper, we present several algorithms that incorporate ensemble diversity into a reinforcement learning (RL)-based ensemble selection framework to build accurate and parsimonious ensembles. These algorithms, as well as several baselines, are rigorously evaluated on datasets from diverse domains in terms of the predictive performance and parsimony of their ensembles. This evaluation demonstrates that our diversity-incorporated RL-based algorithms perform better than the others for constructing simultaneously accurate and parsimonious ensembles. These algorithms can eventually aid the interpretation or reverse engineering of predictive models assimilated into effective ensembles. To enable such a translation, an implementation of these algorithms, as well the experimental setup they are evaluated in, has been made available at https://github.com/GauravPandeyLab/lens-learning-ensembles-using-reinforcement-learning.

Paper PDF Code

Code

GauravPandeyLab/lens-learning-ensembles-using-reinforcement-learning officialmentioned in paper report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

Not run by Syntology. Nothing on this page verifies that the listed code works.

Tasks

Diversity Reinforcement Learning Reinforcement Learning (RL)Translation reinforcement-learning

Results from the paper archive 2025-07-28

No leaderboard rows for this paper in the archive.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections