Apply a rough set-based classifier to dependency parsing
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Data classification using rough sets and naïve Bayes
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
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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.