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Random subspace method

About: Random subspace method is a(n) research topic. Over the lifetime, 3426 publication(s) have been published within this topic receiving 177106 citation(s).

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Journal ArticleDOI
Leo Breiman1Institutions (1)
01 Oct 2001-
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.

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Abstract: Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, aaa, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.

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58,232 citations


Journal ArticleDOI
TL;DR: A common theoretical framework for combining classifiers which use distinct pattern representations is developed and it is shown that many existing schemes can be considered as special cases of compound classification where all the pattern representations are used jointly to make a decision.

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Abstract: We develop a common theoretical framework for combining classifiers which use distinct pattern representations and show that many existing schemes can be considered as special cases of compound classification where all the pattern representations are used jointly to make a decision. An experimental comparison of various classifier combination schemes demonstrates that the combination rule developed under the most restrictive assumptions-the sum rule-outperforms other classifier combinations schemes. A sensitivity analysis of the various schemes to estimation errors is carried out to show that this finding can be justified theoretically.

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5,535 citations


Journal ArticleDOI
Tin Kam Ho1Institutions (1)
TL;DR: 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.

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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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5,108 citations


MonographDOI
02 Jul 2004-
TL;DR: This combining pattern classifiers methods and algorithms helps people to enjoy a good book with a cup of coffee in the afternoon, instead they cope with some harmful virus inside their computer.

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Abstract: Thank you for downloading combining pattern classifiers methods and algorithms. Maybe you have knowledge that, people have look hundreds times for their chosen novels like this combining pattern classifiers methods and algorithms, but end up in infectious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they cope with some harmful virus inside their computer.

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2,613 citations


Journal ArticleDOI
Lei Xu1, Adam Krzyżak1, Ching Y. Suen1Institutions (1)
01 May 1992-
TL;DR: On applying these methods to combine several classifiers for recognizing totally unconstrained handwritten numerals, the experimental results show that the performance of individual classifiers can be improved significantly.

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Abstract: Possible solutions to the problem of combining classifiers can be divided into three categories according to the levels of information available from the various classifiers. Four approaches based on different methodologies are proposed for solving this problem. One is suitable for combining individual classifiers such as Bayesian, k-nearest-neighbor, and various distance classifiers. The other three could be used for combining any kind of individual classifiers. On applying these methods to combine several classifiers for recognizing totally unconstrained handwritten numerals, the experimental results show that the performance of individual classifiers can be improved significantly. For example, on the US zipcode database, 98.9% recognition with 0.90% substitution and 0.2% rejection can be obtained, as well as high reliability with 95% recognition, 0% substitution, and 5% rejection. >

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2,340 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20219
202012
201918
201843
2017158
2016212

Top Attributes

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Topic's top 5 most impactful authors

Robert Sabourin

26 papers, 1.2K citations

Loris Nanni

21 papers, 544 citations

Fabio Roli

20 papers, 2K citations

Robert P. W. Duin

15 papers, 8.2K citations

Bartosz Krawczyk

11 papers, 205 citations