The Strength of Weak Learnability
Machine Learning
Technical Note: \cal Q-Learning
Machine Learning
Original Contribution: Stacked generalization
Neural Networks
Machine Learning
An approach to the automatic design of multiple classifier systems
Pattern Recognition Letters - Special issue on machine learning and data mining in pattern recognition
A streaming ensemble algorithm (SEA) for large-scale classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Cost complexity-based pruning of ensemble classifiers
Knowledge and Information Systems
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
A Survey of Methods for Scaling Up Inductive Algorithms
Data Mining and Knowledge Discovery
Algorithm Selection using Reinforcement Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Pruning and dynamic scheduling of cost-sensitive ensembles
Eighteenth national conference on Artificial intelligence
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Ensemble selection from libraries of models
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Pruning in ordered bagging ensembles
ICML '06 Proceedings of the 23rd international conference on Machine learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Using boosting to prune bagging ensembles
Pattern Recognition Letters
Selective fusion of heterogeneous classifiers
Intelligent Data Analysis
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Ensemble Pruning Via Semi-definite Programming
The Journal of Machine Learning Research
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Online adaptive policies for ensemble classifiers
Neurocomputing
Selective ensemble of decision trees
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Ensemble pruning using reinforcement learning
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
A fast ensemble pruning algorithm based on pattern mining process
Data Mining and Knowledge Discovery
A probabilistic model of classifier competence for dynamic ensemble selection
Pattern Recognition
A competitive ensemble pruning approach based on cross-validation technique
Knowledge-Based Systems
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This paper studies the problem of pruning an ensemble of classifiers from a reinforcement learning perspective. It contributes a new pruning approach that uses the Q-learning algorithm in order to approximate an optimal policy of choosing whether to include or exclude each classifier from the ensemble. Extensive experimental comparisons of the proposed approach against state-of-the-art pruning and combination methods show very promising results. Additionally, we present an extension that allows the improvement of the solutions returned by the proposed approach over time, which is very useful in certain performance-critical domains.