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- W17321162 abstract "We suggest a new framework for classification rule mining in quantitative data sets founded on Bayes theory – without univariate preprocessing of attributes. We introduce a space of rule models and a prior distribution defined on this model space. As a result, we obtain the definition of a parameter-free criterion for classification rules. We show that the new criterion identifies interesting classification rules while being highly resilient to spurious patterns. We develop a new parameter-free algorithm to mine locally optimal classification rules efficiently. The mined rules are directly used as new features in a classification process based on a selective naive Bayes classifier. The resulting classifier demonstrates higher inductive performance than state-of-the-art rule-based classifiers." @default.
- W17321162 created "2016-06-24" @default.
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- W17321162 date "2012-01-01" @default.
- W17321162 modified "2023-09-28" @default.
- W17321162 title "A Bayesian Approach for Classification Rule Mining in Quantitative Databases" @default.
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- W17321162 doi "https://doi.org/10.1007/978-3-642-33486-3_16" @default.
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