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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.
Developing parsimonious ensembles using ensemble diversity within a reinforcement learning framework
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.
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