A Semi-naive Bayes Classifier with Grouping of Cases

Authors:
Joaquín Abellán;Andrés Cano;Andrés R. Masegosa;Serafín Moral
Affiliations:
Department of Computer Science and Artificial Intelligence, University of Granada, Spain;Department of Computer Science and Artificial Intelligence, University of Granada, Spain;Department of Computer Science and Artificial Intelligence, University of Granada, Spain;Department of Computer Science and Artificial Intelligence, University of Granada, Spain
Venue:
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Year:
2007

Citing 6
Cited 0

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

Machine Learning - Special issue on learning with probabilistic representations
Data mining: practical machine learning tools and techniques with Java implementations

Data mining: practical machine learning tools and techniques with Java implementations
Semi-Naive Bayesian Classifier

EWSL '91 Proceedings of the European Working Session on Machine Learning
Not So Naive Bayes: Aggregating One-Dependence Estimators

Machine Learning
Learning Bayesian Networks

Learning Bayesian Networks
Upper entropy of credal sets. Applications to credal classification

International Journal of Approximate Reasoning

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this work, we present a semi-naive Bayes classifier that searches for dependent attributes using different filter approaches. In order to avoid that the number of cases of the compound attributes be too high, a grouping procedure is applied each time after two variables are merged. This method tries to group two or more cases of the new variable into an unique value. In an emperical study, we show as this approach outperforms the naive Bayes classifier in a very robust way and reaches the performance of the Pazzani's semi-naive Bayes [1] without the high cost of a wrapper search.