Propositionalized attribute taxonomies from data for data-driven construction of concise classifiers

@article{Kang2011PropositionalizedAT,
  title={Propositionalized attribute taxonomies from data for data-driven construction of concise classifiers},
  author={Dae-Ki Kang and Myoung-Jong Kim},
  journal={Expert Syst. Appl.},
  year={2011},
  volume={38},
  pages={12739-12746}
}
In this paper, we consider the problem of generating concise but accurate naive Bayes classifiers using taxonomy of propositionalized attributes. For the problem, we introduce propositionalized attribute taxonomy guided naive Bayes Learner (PAT-NBL), a machine learning algorithm that effectively utilizes taxonomy to generate compact classifiers. We extend classical naive Bayes learner to the PAT-NBL algorithm that traverses over a propositionalized taxonomy to search for a locally optimal cut… CONTINUE READING

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