The Strength of Weak Learnability
Machine Learning
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
The Mathematics of Generalization: The Proceedings of the SFI/CNLS Workshop on Formal Approaches to Supervised 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
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)
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
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
Ensemble pruning via individual contribution ordering
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Greedy optimization classifiers ensemble based on diversity
Pattern Recognition
Adaptive ROC-based ensembles of HMMs applied to anomaly detection
Pattern Recognition
Ensemble pruning via base-classifier replacement
WAIM'11 Proceedings of the 12th international conference on Web-age information management
A new metric for greedy ensemble pruning
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part II
Energy-Based metric for ensemble selection
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
Diversity regularized ensemble pruning
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
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Ensemble selection deals with the reduction of an ensemble of predictive models in order to improve its efficiency and predictive performance. A number of ensemble selection methods that are based on greedy search of the space of all possible ensemble subsets have recently been proposed. This paper contributes a novel method, based on a new diversity measure that takes into account the strength of the decision of the current ensemble. Experimental comparison of the proposed method, dubbed Focused Ensemble Selection (FES), against state-of-the-art greedy ensemble selection methods shows that it leads to small ensembles with high predictive performance.