C4.5: programs for machine learning
C4.5: programs for machine learning
Approximation algorithms for NP-hard problems
Approximation algorithms for NP-hard problems
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
Improved boosting algorithms using confidence-rated predictions
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Polynomially bounded minimization problems that are hard to approximate
Nordic Journal of Computing
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Efficiently Approximating Weighted Sums with Exponentially Many Terms
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
Decision Templates Based RBF Network for Tree-Structured Multiple Classifier Fusion
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Ensemble pruning via individual contribution ordering
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Ensemble pruning via base-classifier replacement
WAIM'11 Proceedings of the 12th international conference on Web-age information management
Eigenclassifiers for combining correlated classifiers
Information Sciences: an International Journal
Margin distribution based bagging pruning
Neurocomputing
Pruning adaptive boosting ensembles by means of a genetic algorithm
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Energy-Based metric for ensemble selection
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
Ensemble approaches for regression: A survey
ACM Computing Surveys (CSUR)
A competitive ensemble pruning approach based on cross-validation technique
Knowledge-Based Systems
Diversity regularized ensemble pruning
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Malware detection by pruning of parallel ensembles using harmony search
Pattern Recognition Letters
An effective ensemble pruning algorithm based on frequent patterns
Knowledge-Based Systems
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Boosting is a powerful method for improving the predictive accuracy of classifiers. The ADABOOST algorithm of Freund and Schapire has been successfully applied to many domains [2, 10, 12] and the combination of ADABOOST with the C4.5 decision tree algorithm has been called the best off-the-shelf learning algorithm in practice. Unfortunately, in some applications, the number of decision trees required by ADABOOST to achieve a reasonable accuracy is enormously large and hence is very space consuming. This problem was first studied by Margineantu and Dietterich [7], where they proposed an empirical method called Kappa pruning to prune the boosting ensemble of decision trees. The Kappa method did this without sacrificing too much accuracy. In this work-in-progress we propose a potential improvement to the Kappa pruning method and also study the boosting pruning problem from a theoretical perspective. We point out that the boosting pruning problem is intractable even to approximate. Finally, we suggest a margin-based theoretical heuristic for this problem.