Ensemble selection for superparent-one-dependence estimators

Authors:
Ying Yang;Kevin Korb;Kai Ming Ting;Geoffrey I. Webb
Affiliations:
School of Computer Science and Software Engineering, Faculty of Information Technology, Monash University, VIC, Australia;School of Computer Science and Software Engineering, Faculty of Information Technology, Monash University, VIC, Australia;School of Computer Science and Software Engineering, Faculty of Information Technology, Monash University, VIC, Australia;School of Computer Science and Software Engineering, Faculty of Information Technology, Monash University, VIC, Australia
Venue:
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Year:
2005

Citing 3
Cited 7

Bayesian Network Classifiers

Machine Learning - Special issue on learning with probabilistic representations
Bayesian Artificial Intelligence

Bayesian Artificial Intelligence
Not So Naive Bayes: Aggregating One-Dependence Estimators

Machine Learning

Efficient lazy elimination for averaged one-dependence estimators

ICML '06 Proceedings of the 23rd international conference on Machine learning
To Select or To Weigh: A Comparative Study of Linear Combination Schemes for SuperParent-One-Dependence Estimators

IEEE Transactions on Knowledge and Data Engineering
HODE: Hidden One-Dependence Estimator

ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Scaling up the accuracy of Bayesian classifier based on frequent itemsets by m-estimate

AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part I
To select or to weigh: a comparative study of model selection and model weighing for SPODE ensembles

ECML'06 Proceedings of the 17th European conference on Machine Learning
Margin-based ordered aggregation for ensemble pruning

Pattern Recognition Letters
Improving classifications for cardiac autonomic neuropathy using multi-level ensemble classifiers and feature selection based on random forest

AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134

Quantified Score

Hi-index	0.00

Visualization

Abstract

SuperParent-One-Dependence Estimators (SPODEs) loosen Naive-Bayes’ attribute independence assumption by allowing each attribute to depend on a common single attribute (superparent) in addition to the class. An ensemble of SPODEs is able to achieve high classification accuracy with modest computational cost. This paper investigates how to select SPODEs for ensembling. Various popular model selection strategies are presented. Their learning efficacy and efficiency are theoretically analyzed and empirically verified. Accordingly, guidelines are investigated for choosing between selection criteria in differing contexts.