Developing parsimonious ensembles using predictor diversity within a reinforcement learning framework

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.

Developing parsimonious ensembles using ensemble diversity within a reinforcement learning framework

Heterogeneous ensembles built from the predictions of a wide variety and large number of diverse base predictors represent a potent approach to building predictive models for problems where the ideal base/individual predictor may not be obvious. Ensemble selection is an especially promising approach here, not only for improving prediction performance, but also because of its ability to select a collectively predictive subset, often a relatively small one, of the base predictors. In this paper, we present a set of algorithms that explicitly incorporate ensemble diversity, a known factor influencing predictive performance of ensembles, into a reinforcement learning framework for ensemble selection. We rigorously tested these approaches on several challenging problems and associated data sets, yielding that several of them produced more accurate ensembles than those that don't explicitly consider diversity. More importantly, these diversity-incorporating ensembles were much smaller in size, i.e., more parsimonious, than the latter types of ensembles. This can eventually aid the interpretation or reverse engineering of predictive models assimilated into the resultant ensemble(s).