Sampling of virtual examples to improve classification accuracy for nominal attribute data

Authors:
Yujung Lee;Jaeho Kang;Byoungho Kang;Kwang Ryel Ryu
Affiliations:
Department of Computer Engineering, Pusan National University, Busan, Korea;Department of Computer Engineering, Pusan National University, Busan, Korea;Department of Computer Engineering, Pusan National University, Busan, Korea;Department of Computer Engineering, Pusan National University, Busan, Korea
Venue:
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Year:
2006

Citing 15
Cited 0

Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms

International Journal of Man-Machine Studies - Special issue: symbolic problem solving in noisy and novel task environments
Original Contribution: Stacked generalization

Neural Networks
C4.5: programs for machine learning

C4.5: programs for machine learning
Bagging predictors

Machine Learning
Bayesian Network Classifiers

Machine Learning - Special issue on learning with probabilistic representations
Fast training of support vector machines using sequential minimal optimization

Advances in kernel methods
Data mining: practical machine learning tools and techniques with Java implementations

Data mining: practical machine learning tools and techniques with Java implementations
Text Classification from Labeled and Unlabeled Documents using EM

Machine Learning - Special issue on information retrieval
Toward Optimal Active Learning through Sampling Estimation of Error Reduction

ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Structural extension to logistic regression: discriminative parameter learning of belief net classifiers

Eighteenth national conference on Artificial intelligence
Learning Bayesian network classifiers by maximizing conditional likelihood

ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning class-discriminative dynamic Bayesian networks

ICML '05 Proceedings of the 22nd international conference on Machine learning
Off-Line Handwritten Character Recognition by SVM on the Virtual Examples Synthesized from On-Line Characters

ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Learning with many irrelevant features

AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
Estimating continuous distributions in Bayesian classifiers

UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a method of using virtual examples to improve the classification accuracy for data with nominal attributes. Most of the previous researches on virtual examples focused on data with numeric attributes, and they used domain-specific knowledge to generate useful virtual examples for a particularly targeted learning algorithm. Instead of using domain-specific knowledge, our method samples virtual examples from a naïve Bayesian network constructed from the given training set. A sampled example is considered useful if it contributes to the increment of the network’s conditional likelihood when added to the training set. A set of useful virtual examples can be collected by repeating this process of sampling followed by evaluation. Experiments have shown that the virtual examples collected this way can help various learning algorithms to derive classifiers of improved accuracy.