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Generation and Selection of Nominal Virtual Examples for Improving the Classifier Performance  

Lee, Yu-Jung (부산대학교 컴퓨터공학과)
Kang, Byoung-Ho (부산대학교 컴퓨터공학과)
Kang, Jae-Ho (야후코리아 Search R&D센터)
Ryu, Kwang-Ryel (부산대학교 컴퓨터공학과)
Publication Information
Journal of KIISE:Software and Applications / v.33, no.12, 2006 , pp. 1052-1061 More about this Journal
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 naive 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.
Keywords
machine learning; classification; Bayesian network; naive Bayes; conditional likelihood; virtual example;
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Times Cited By KSCI : 1  (Citation Analysis)
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