Non-Disjoint discretization for aggregating one-dependence estimator classifiers

Authors:
Ana M. Martínez;Geoffrey I. Webb;M. Julia Flores;José A. Gámez
Affiliations:
Computer Systems Department, Intelligent Systems & Data Mining, University of Castilla-La Mancha, Albacete, Spain;Faculty of Information Technology, Monash University, Melbourne, Australia;Computer Systems Department, Intelligent Systems & Data Mining, University of Castilla-La Mancha, Albacete, Spain;Computer Systems Department, Intelligent Systems & Data Mining, University of Castilla-La Mancha, Albacete, Spain
Venue:
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
Year:
2012

Citing 12
Cited 0

On the Optimality of the Simple Bayesian Classifier under Zero-One Loss

Machine Learning - Special issue on learning with probabilistic representations
Approximate statistical tests for comparing supervised classification learning algorithms

Neural Computation
Lazy Learning of Bayesian Rules

Machine Learning
Non-Disjoint Discretization for Naive-Bayes Classifiers

ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Proportional k-Interval Discretization for Naive-Bayes Classifiers

EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Not So Naive Bayes: Aggregating One-Dependence Estimators

Machine Learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)

Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Statistical Comparisons of Classifiers over Multiple Data Sets

The Journal of Machine Learning Research
Discretization for naive-Bayes learning: managing discretization bias and variance

Machine Learning
GAODE and HAODE: two proposals based on AODE to deal with continuous variables

ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Handling numeric attributes when comparing Bayesian network classifiers: does the discretization method matter?

Applied Intelligence
Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive Bayesian classification

Machine Learning

Quantified Score

Hi-index	0.00

Visualization

Abstract

There is still lack of clarity about the best manner in which to handle numeric attributes when applying Bayesian network classifiers. Discretization methods entail an unavoidable loss of information. Nonetheless, a number of studies have shown that appropriate discretization can outperform straightforward use of common, but often unrealistic parametric distribution (e.g. Gaussian). Previous studies have shown the Averaged One-Dependence Estimators (AODE) classifier and its variant Hybrid AODE (HAODE, which deals with numeric and discrete variables) to be robust towards the discretization method applied. However, all the discretization techniques taken into account so far formed non-overlapping intervals for a numeric attribute. We argue that the idea of non-disjoint discretization, already justified in Naive Bayes classifiers, can also be profitably extended to AODE and HAODE, albeit with some variations; and our experimental results seem to support this hypothesis, specially for the latter.