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
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Machine Learning
Ensembling neural networks: many could be better than all
Artificial Intelligence
Linear Programming Boosting via Column Generation
Machine Learning
On the Boosting Pruning Problem
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Pruning in ordered bagging ensembles
ICML '06 Proceedings of the 23rd international conference on Machine learning
Ensemble Pruning Via Semi-definite Programming
The Journal of Machine Learning Research
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Issues in stacked generalization
Journal of Artificial Intelligence Research
Selective ensemble of decision trees
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Designing ensembles of fuzzy classification systems: an immune-inspired approach
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
On the use of selective ensembles for relevance classification in case-based web search
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
Combining classifiers using nearest decision prototypes
Applied Soft Computing
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Multiclass pattern recognition problems (K 2) can be decomposed by a tree-structured approach. It constructs an ensemble of K -1 individually trained binary classifiers whose predictions are combined to classify unseen instances. A key factor for an effective ensemble is how to combine its member outputs to give the final decision. Although there are various methods to build the tree structure and to solve the underlying binary problems, there is not much work to develop new combination methods that can best combine these intermediate results. We present here a trainable fusion method that integrates statistical information about the individual outputs (clustered decision templates) into a Radial Basis Function (RBF) network. We compare our model with the decision templates combiner and the existing nontrainable tree ensemble fusion methods: classical decision tree-like approach, product of the unique path and Dempster-Shafer evidence theory based method.