The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
The random subspace method for constructing decision forests
Open accessAbstract
Much of previous attention on decision trees focuses on the splitting criteria and optimization of tree sizes. The dilemma between overfitting and achieving maximum accuracy is seldom resolved. A method to construct a decision tree based classifier is proposed that maintains highest accuracy on training data and improves on generalization accuracy as it grows in complexity. The classifier consists of multiple trees constructed systematically by pseudorandomly selecting subsets of components of the feature vector, that is, trees constructed in randomly chosen subspaces. The subspace method is compared to single-tree classifiers and other forest construction methods by experiments on publicly available datasets, where the method's superiority is demonstrated. We also discuss independence between trees in a forest and relate that to the combined classification accuracy.
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Cited by
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Geurts, P., Ernst, D., & Wehenkel, L. (2006) · Machine Learning · doi:10.1007/s10994-006-6226-1 · Cited by 9,300
Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)
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Metadata
- Authors
- Ho, T. K.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 1998
- Volume
- 20
- Issue
- 8
- Pages
- 832-844
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2113242816
- Cited by
- 6,842
- References
- 3
- FWCI
- 37.91
- Type
- journal article
- Language
- en
- Open access
- green
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