Orthogonal decision trees
| Autor: | Haimonti Dutta, K. Kargupta, B.-H. Park |
|---|---|
| Rok vydání: | 2006 |
| Předmět: |
Boosting (machine learning)
Computer science Data stream mining Supervised learning Decision tree computer.software_genre Computer Science Applications Random forest Computational Theory and Mathematics Principal component analysis Alternating decision tree Data mining computer Information Systems Decision analysis |
| Zdroj: | IEEE Transactions on Knowledge and Data Engineering. 18:1028-1042 |
| ISSN: | 1041-4347 |
| DOI: | 10.1109/tkde.2006.127 |
| Popis: | 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 the Fourier transformation of decision trees and eigen-analysis of the ensemble in the Fourier representation. It offers experimental results to document the performance of orthogonal trees on the grounds of accuracy and model complexity |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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