Original Contribution: Stacked generalization
Neural Networks
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Machine Learning - Special issue on inductive transfer
The application of AdaBoost for distributed, scalable and on-line learning
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
The distributed boosting algorithm
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A streaming ensemble algorithm (SEA) for large-scale classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distributed Data Mining in Credit Card Fraud Detection
IEEE Intelligent Systems
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Ensemble selection from libraries of models
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning when training data are costly: the effect of class distribution on tree induction
Journal of Artificial Intelligence Research
Application of tree mining to matching of knowledge structures of decision tree type
OTM'07 Proceedings of the 2007 OTM Confederated international conference on On the move to meaningful internet systems - Volume Part II
Hi-index | 0.00 |
A classification ensemble is a group of classifiers that all solve the same prediction problem in different ways. It is well-known that combining the predictions of classifiers within the same problem domain using techniques like bagging or boosting often improves the performance. This research shows that sharing classifiers among different but closely related problem domains can also be helpful. In addition, a semi-definite programming based ensemble pruning method is implemented in order to optimize the selection of a subset of classifiers for each problem domain. Computational results on a catalog dataset indicate that the ensembles resulting from sharing classifiers among different product categories generally have larger AUCs than those ensembles trained only on their own categories. The pruning algorithm not only prevents the occasional decrease of effectiveness caused by conflicting concepts among the problem domains, but also provides a better understanding of the problem domains and their relationships.