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Published Articles >> Table of Contents >> Abstract
Fourth IEEE International Conference on Data Mining (ICDM'04)
pp. 427-430
Orthogonal Decision Trees
Hillol Kargupta, University of Maryland Baltimore County
Haimonti Dutta, University of Maryland Baltimore County
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2004.10072
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| Abstract |
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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.
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Citation:
Hillol Kargupta, Haimonti Dutta,
"Orthogonal Decision Trees,"
icdm,
pp. 427-430,
Fourth IEEE International Conference on Data Mining (ICDM'04),
2004
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