A Bayesian Approach for Classification Rule Mining in Quantitative Databases
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.
Cite
Text
Gay and Boullé. "A Bayesian Approach for Classification Rule Mining in Quantitative Databases." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2012. doi:10.1007/978-3-642-33486-3_16Markdown
[Gay and Boullé. "A Bayesian Approach for Classification Rule Mining in Quantitative Databases." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2012.](https://mlanthology.org/ecmlpkdd/2012/gay2012ecmlpkdd-bayesian/) doi:10.1007/978-3-642-33486-3_16BibTeX
@inproceedings{gay2012ecmlpkdd-bayesian,
title = {{A Bayesian Approach for Classification Rule Mining in Quantitative Databases}},
author = {Gay, Dominique and Boullé, Marc},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2012},
pages = {243-259},
doi = {10.1007/978-3-642-33486-3_16},
url = {https://mlanthology.org/ecmlpkdd/2012/gay2012ecmlpkdd-bayesian/}
}