Resource aware distributed knowledge discovery
Ubiquitous knowledge discovery
Resource aware distributed knowledge discovery
Ubiquitous knowledge discovery
Decisions: algebra and implementation
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
HiPC'05 Proceedings of the 12th international conference on High Performance Computing
Mining global association rules on an oracle grid by scanning once distributed databases
Euro-Par'05 Proceedings of the 11th international Euro-Par conference on Parallel Processing
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This paper introduces orthogonal decision trees that offer an effective way to construct a redundancy-free, accurate, and meaningful representation of large decision-tree-ensembles often created by popular techniques such as Bagging, Boosting, Random Forests and many distributed and data stream mining algorithms. Orthogonal decision trees are functionally orthogonal to each other and they correspond to the principal components of the underlying function space. This paper offers a technique to construct such trees based on eigen-analysis of the ensemble and offers experimental results to document the performance of orthogonal trees on grounds of accuracy and model complexity.