The Strength of Weak Learnability
Machine Learning
Original Contribution: Stacked generalization
Neural Networks
C4.5: programs for machine learning
C4.5: programs for machine learning
Boosting a weak learning algorithm by majority
Information and Computation
Machine Learning
Decision Tree Induction Based on Efficient Tree Restructuring
Machine Learning
General bounds on statistical query learning and PAC learning with noise via hypothesis boosting
Information and Computation
On the boosting ability of top-down decision tree learning algorithms
Journal of Computer and System Sciences
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Machine Learning
Machine Learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A combined neural network and decision trees model for prognosis of breast cancer relapse
Artificial Intelligence in Medicine
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An active research area in Machine Learning is the construction of multiple classifier systems to increase learning accuracy of simple classifiers. In this paper we present E-CIDIM, a multiple classifier system designed to improve the performance of CIDIM, an algorithm that induces small and accurate decision trees. E-CIDIM keeps a maximum number of trees and it induces new trees that may substitute the old trees in the ensemble. The substitution process finishes when none of the new trees improves the accuracy of any of the trees in the ensemble after a pre-configured number of attempts. In this way, the accuracy obtained thanks to an unique instance of CIDIM can be improved. In reference to the accuracy of the generated ensembles, E-CIDIM competes well against bagging and boosting at statistically significance confidence levels and it usually outperforms them in the accuracy and the average size of the trees in the ensemble.