A Discriminative Learning Method of TAN Classifier

Authors:
Qi Feng;Fengzhan Tian;Houkuan Huang
Affiliations:
School of Computer & Information Technology, Beijing Jiaotong University, Beijing, China 100044;School of Computer & Information Technology, Beijing Jiaotong University, Beijing, China 100044;School of Computer & Information Technology, Beijing Jiaotong University, Beijing, China 100044
Venue:
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Year:
2007

Citing 12
Cited 0

Elements of information theory

Elements of information theory
A Bayesian Method for the Induction of Probabilistic Networks from Data

Machine Learning
The nature of statistical learning theory

The nature of statistical learning theory
Bayesian Network Classifiers

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
Machine Learning

Machine Learning
Dynamic Bayesian Multinets

UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Structural extension to logistic regression: discriminative parameter learning of belief net classifiers

Eighteenth national conference on Artificial intelligence
Natural statistical models for automatic speech recognition

Natural statistical models for automatic speech recognition
Learning Bayesian network classifiers by maximizing conditional likelihood

ICML '04 Proceedings of the twenty-first international conference on Machine learning
Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques
Discriminative versus generative parameter and structure learning of Bayesian network classifiers

ICML '05 Proceedings of the 22nd international conference on Machine learning

Quantified Score

Hi-index	0.00

Visualization

Abstract

TAN (Tree-augmented Naïve Bayes) classifier makes a compromise between the model complexity and classification rate, the study of which has now become a hot research issue. In this paper, we propose a discriminative method that is based on KL (Kullback-Leibler) divergence to learn TAN classifier. First, we use EAR (explaining away residual) method to learn the structure of TAN, and then optimize TAN parameters by an objective function based on KL divergence. The results of the experiments on benchmark datasets show that our approach produces better classification rate.