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Cited by 6,842

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 access
Tin Kam Ho·1998·IEEE Transactions on Pattern Analysis and Machine Intelligence
DOI 10.1109/34.709601
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Abstract

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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Research works printed but not linked: 29

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Cited by

Metadata

Authors
Ho, T. K.
Venue
IEEE Transactions on Pattern Analysis and Machine Intelligence
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
1998
Volume
20
Issue
8
Pages
832-844
ISSN
0162-8828 (ISSN-L)
DOI
10.1109/34.709601
OpenAlex
W2113242816
Cited by
6,842
References
3
FWCI
37.91
Type
journal article
Language
en
Open access
green
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)

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