Deakin home > Deakin University Library > Deakin Research Online > Proportional k-interval discretization for naive-Bayes classifiers

Proportional k-interval discretization for naive-Bayes classifiers

Yang, Ying and Webb, Geoffrey I. 2001, Proportional k-interval discretization for naive-Bayes classifiers, in ECML 2001 : Machine Learning : 12th European Conference on Machine Learning, Springer-Verlag, Berlin, Germany, pp. 564-575.

Attached Files (Some files may be inaccessible until you login with your Deakin Research Online credentials)
Name Description MIMEType Size Downloads

Title Proportional k-interval discretization for naive-Bayes classifiers
Author(s) Yang, Ying
Webb, Geoffrey I.
Conference name European Conference on Machine Learning (12th : 2001 : Freiburg, Germany)
Conference location Freiburg, Germany
Conference dates 3-7 September 2001
Title of proceedings ECML 2001 : Machine Learning : 12th European Conference on Machine Learning
Editor(s) Carbonell, Jaime G.
Siekmann, Jorg
Publication date 2001
Series Lecture notes in computer science ; 2167
Start page 564
End page 575
Publisher Springer-Verlag
Place of publication Berlin, Germany
Summary This paper argues that two commonly-used discretization approaches, fixed k-interval discretization and entropy-based discretization have sub-optimal characteristics for naive-Bayes classification. This analysis leads to a new discretization method, Proportional k-Interval Discretization (PKID), which adjusts the number and size of discretized intervals to the number of training instances, thus seeks an appropriate trade-off between the bias and variance of the probability estimation for naive-Bayes classifiers. We justify PKID in theory, as well as test it on a wide cross-section of datasets. Our experimental results suggest that in comparison to its alternatives, PKID provides naive-Bayes classifiers competitive classification performance for smaller datasets and better classification performance for larger datasets.
ISBN 3540425365
9783540425366
Language eng
Field of Research 080199 Artificial Intelligence and Image Processing not elsewhere classified
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category E1 Full written paper - refereed
Copyright notice ©Springer-Verlag Berlin Heidelberg 2001
Persistent URL http://hdl.handle.net/10536/DRO/DU:30004438

Document type: Conference Paper
Collection: School of Information Technology
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in Deakin Research Online is owned by the author, with all rights reserved.

Versions
Version Filter Type
Access Statistics: 279 Abstract Views, 0 File Downloads  -  Detailed Statistics
Created: Mon, 07 Jul 2008, 09:37:14 EST