Experimental comparison of classification uncertainty for randomised and Bayesian decision tree ensembles

Vitaly Schetinin; Derek Partridge; Wojtek J. Krzanowski; Richard M. Everson; Jonathan E. Fieldsend; Trevor C. Bailey; Adolfo Hernandez

doi:10.1007/978-3-540-28651-6_108

Experimental comparison of classification uncertainty for randomised and Bayesian decision tree ensembles

Vitaly Schetinin
, Derek Partridge
, Wojtek J. Krzanowski
, Richard M. Everson
, Jonathan E. Fieldsend
, Trevor C. Bailey
, Adolfo Hernandez

Institute for Research in Applicable Computing (IRAC)

University of Exeter

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

5 Citations (Scopus)

Abstract

In this paper we experimentally compare the classification uncertainty of the randomised Decision Tree (DT) ensemble technique and the Bayesian DT technique with a restarting strategy on a synthetic dataset as well as on some datasets commonly used in the machine learning community. For quantitative evaluation of classification uncertainty, we use an Uncertainty Envelope dealing with the class posterior distribution and a given confidence probability. Counting the classifier outcomes, this technique produces feasible evaluations of the classification uncertainty. Using this technique in our experiments, we found that the Bayesian DT technique is superior to the randomised DT ensemble technique.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Editors	Zheng Rong Yang, Richard Everson, Hujun Yin
Publisher	Springer
Pages	726-732
Number of pages	7
ISBN (Print)	3540228810, 9783540228813
DOIs	https://doi.org/10.1007/978-3-540-28651-6_108
Publication status	Published - 2004

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3177
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

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Schetinin, V., Partridge, D., Krzanowski, W. J., Everson, R. M., Fieldsend, J. E., Bailey, T. C., & Hernandez, A. (2004). Experimental comparison of classification uncertainty for randomised and Bayesian decision tree ensembles. In Z. R. Yang, R. Everson, & H. Yin (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 726-732). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3177). Springer. https://doi.org/10.1007/978-3-540-28651-6_108

Schetinin, Vitaly ; Partridge, Derek ; Krzanowski, Wojtek J. et al. / Experimental comparison of classification uncertainty for randomised and Bayesian decision tree ensembles. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). editor / Zheng Rong Yang ; Richard Everson ; Hujun Yin. Springer, 2004. pp. 726-732 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Experimental comparison of classification uncertainty for randomised and Bayesian decision tree ensembles",

abstract = "In this paper we experimentally compare the classification uncertainty of the randomised Decision Tree (DT) ensemble technique and the Bayesian DT technique with a restarting strategy on a synthetic dataset as well as on some datasets commonly used in the machine learning community. For quantitative evaluation of classification uncertainty, we use an Uncertainty Envelope dealing with the class posterior distribution and a given confidence probability. Counting the classifier outcomes, this technique produces feasible evaluations of the classification uncertainty. Using this technique in our experiments, we found that the Bayesian DT technique is superior to the randomised DT ensemble technique.",

author = "Vitaly Schetinin and Derek Partridge and Krzanowski, \{Wojtek J.\} and Everson, \{Richard M.\} and Fieldsend, \{Jonathan E.\} and Bailey, \{Trevor C.\} and Adolfo Hernandez",

year = "2004",

doi = "10.1007/978-3-540-28651-6\_108",

language = "English",

isbn = "3540228810",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "726--732",

editor = "Yang, \{Zheng Rong\} and Richard Everson and Hujun Yin",

booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

address = "Germany",

}

Schetinin, V, Partridge, D, Krzanowski, WJ, Everson, RM, Fieldsend, JE, Bailey, TC & Hernandez, A 2004, Experimental comparison of classification uncertainty for randomised and Bayesian decision tree ensembles. in ZR Yang, R Everson & H Yin (eds), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3177, Springer, pp. 726-732. https://doi.org/10.1007/978-3-540-28651-6_108

Experimental comparison of classification uncertainty for randomised and Bayesian decision tree ensembles. / Schetinin, Vitaly; Partridge, Derek; Krzanowski, Wojtek J. et al.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). ed. / Zheng Rong Yang; Richard Everson; Hujun Yin. Springer, 2004. p. 726-732 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3177).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

TY - CHAP

T1 - Experimental comparison of classification uncertainty for randomised and Bayesian decision tree ensembles

AU - Schetinin, Vitaly

AU - Partridge, Derek

AU - Krzanowski, Wojtek J.

AU - Everson, Richard M.

AU - Fieldsend, Jonathan E.

AU - Bailey, Trevor C.

AU - Hernandez, Adolfo

PY - 2004

Y1 - 2004

N2 - In this paper we experimentally compare the classification uncertainty of the randomised Decision Tree (DT) ensemble technique and the Bayesian DT technique with a restarting strategy on a synthetic dataset as well as on some datasets commonly used in the machine learning community. For quantitative evaluation of classification uncertainty, we use an Uncertainty Envelope dealing with the class posterior distribution and a given confidence probability. Counting the classifier outcomes, this technique produces feasible evaluations of the classification uncertainty. Using this technique in our experiments, we found that the Bayesian DT technique is superior to the randomised DT ensemble technique.

AB - In this paper we experimentally compare the classification uncertainty of the randomised Decision Tree (DT) ensemble technique and the Bayesian DT technique with a restarting strategy on a synthetic dataset as well as on some datasets commonly used in the machine learning community. For quantitative evaluation of classification uncertainty, we use an Uncertainty Envelope dealing with the class posterior distribution and a given confidence probability. Counting the classifier outcomes, this technique produces feasible evaluations of the classification uncertainty. Using this technique in our experiments, we found that the Bayesian DT technique is superior to the randomised DT ensemble technique.

UR - https://www.scopus.com/pages/publications/35048895411

U2 - 10.1007/978-3-540-28651-6_108

DO - 10.1007/978-3-540-28651-6_108

M3 - Chapter

AN - SCOPUS:35048895411

SN - 3540228810

SN - 9783540228813

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 726

EP - 732

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

A2 - Yang, Zheng Rong

A2 - Everson, Richard

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PB - Springer

ER -

Schetinin V, Partridge D, Krzanowski WJ, Everson RM, Fieldsend JE, Bailey TC et al. Experimental comparison of classification uncertainty for randomised and Bayesian decision tree ensembles. In Yang ZR, Everson R, Yin H, editors, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer. 2004. p. 726-732. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-28651-6_108

Experimental comparison of classification uncertainty for randomised and Bayesian decision tree ensembles

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