The Strength of Weak Learnability
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
A Dynamic Integration Algorithm for an Ensemble of Classifiers
ISMIS '99 Proceedings of the 11th International Symposium on Foundations of Intelligent Systems
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Data Mining using MLC++, A Machine Learning Library in C++
ICTAI '96 Proceedings of the 8th International Conference on Tools with Artificial Intelligence
A brief introduction to boosting
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
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Decision committee learning has demonstrated outstanding success in reducing classification error with an ensemble of classifiers. In a way a decision committee is a classifier formed upon an ensemble of subsidiary classifiers. Voting, which is commonly used to produce the final decision of committees has, however, a shortcoming. It is unable to take into account local expertise. When a new instance is difficult to classify, then it easily happens that only the minority of the classifiers will succeed, and the majority voting will quite probably result in a wrong classification. We suggest that dynamic integration of classifiers is used instead of majority voting in decision committees. Our method is based on the assumption that each classifier is best inside certain subareas of the whole domain. In this paper, the proposed dynamic integration is evaluated in combination with the well-known decision committee approaches AdaBoost and Bagging. The comparison results show that both boosting and bagging produce often significantly higher accuracy with the dynamic integration than with voting.