A Comparative Study of Semi-naive Bayes Methods in Classification Learning

@inproceedings{Zheng2005ACS,
  title={A Comparative Study of Semi-naive Bayes Methods in Classification Learning},
  author={Fei Zheng and Geoffrey I. Webb},
  year={2005}
}
Numerous techniques have sought to improve the accuracy of Naive Bayes (NB) by alleviating the attribute interdependence problem. This paper summarizes these semi-naive Bayesian methods into two groups: those that apply conventional NB with a new attribute set, and those that alter NB by allowing inter-dependencies between attributes. We review eight typical semi-naive Bayesian learning algorithms and perform error analysis using the bias-variance decomposition on thirty-six natural domains… CONTINUE READING
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