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Ensemble learning combines multiple classifiers in the hope of obtainingbetter predictive performance. Empirical studies have shown that ensemblepruning, that is, choosing an appropriate subset of the available classifiers,can lead to comparable or better predictions than using all classifiers. Inthis paper, we consider a binary classification problem and propose an integerprogramming (IP) approach for selecting optimal classifier subsets. We proposea flexible objective function to adapt to desired criteria of differentdatasets. We also propose constraints to ensure minimum diversity levels in theensemble. Despite the general case of IP being NP-Hard, state-of-the-artsolvers are able to quickly obtain good solutions for datasets with up to 60000data points. Our approach yields competitive results when compared to some ofthe best and most used pruning methods in literature.

Ensemble pruning via an integer programming approach with diversity constraints