The Strength of Weak Learnability
Machine Learning
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Ensembling neural networks: many could be better than all
Artificial Intelligence
On the Boosting Pruning Problem
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Pruning in ordered bagging ensembles
ICML '06 Proceedings of the 23rd international conference on Machine learning
Using boosting to prune bagging ensembles
Pattern Recognition Letters
Ensemble Pruning Via Semi-definite Programming
The Journal of Machine Learning Research
EROS: Ensemble rough subspaces
Pattern Recognition
A fast ensemble pruning algorithm based on pattern mining process
Data Mining and Knowledge Discovery
Ensemble pruning via individual contribution ordering
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Random feature weights for decision tree ensemble construction
Information Fusion
Lung cancer cell identification based on artificial neural network ensembles
Artificial Intelligence in Medicine
Stability problems with artificial neural networks and the ensemble solution
Artificial Intelligence in Medicine
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Ensemble pruning is crucial for the consideration of both predictive accuracy and predictive efficiency. Previous ensemble methods demand vast memory spaces and heavy computational burdens in dealing with large-scale datasets, which leads to the inefficiency for the problem of classification. To address the issue, this paper proposes a novel ensemble pruning algorithm based on the mining of frequent patterns called EP-FP. The method maps the dataset and pruned ensemble to a transactional database in which each transaction corresponds to an instance and each item corresponds to a base classifier. Moreover, a Boolean matrix called as the classification matrix is used to compress the classification resulted by pruned ensemble on the dataset. Henceforth, we transform the problem of ensemble pruning to the mining of frequent base classifiers on the classification matrix. Several candidate ensembles are obtained through extracting base classifiers with better performance iteratively and incrementally. Finally, we determine the final ensemble according to a designed evaluation function. The comparative experiments have demonstrated the effectiveness and validity of EP-FP algorithm for the classification of large-scale datasets.