2007 IEEE International Conference on Granular Computing (GRC 2007) RoughTree A Classifier with Naive-Bayes and Rough Sets Hybrid in Decision Tree Representation San Jose, California November 02-November 04 ISBN: 0-7695-3032-X
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/GrC.2007.52
This paper presents a semi-naive classifier named RoughTree, which is designed to alleviate the attribute interdependence problem of Naive Bayesian Classifier. RoughTree uses the attribute dependence detecting measure in Rough Sets and splits the dataset into subspaces according to the selected attributes, which hold the maximum values by the attribute dependence measure. This process continues the same way a decision tree splits until the stopping criterion is satisfied. Then, the result is a tree-like model and each leaf in the RoughTree is replaced by a Naive-Bayesian classifier. RoughTree eliminates the attribute dependences in its leaves and the experimental results show that RoughTree can achieve better performance than Naive Bayesian classifier.
Citation:
Yangsheng Ji, Lin Shang, "RoughTree A Classifier with Naive-Bayes and Rough Sets Hybrid in Decision Tree Representation," grc, pp.221, 2007 IEEE International Conference on Granular Computing (GRC 2007), 2007 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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