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

  title={A Comparative Study of Semi-naive Bayes Methods in Classification Learning},
  author={Fei Zheng and Geoffrey I. Webb},
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
Highly Cited
This paper has 51 citations. REVIEW CITATIONS


Publications citing this paper.
Showing 1-10 of 32 extracted citations

52 Citations

Citations per Year
Semantic Scholar estimates that this publication has 52 citations based on the available data.

See our FAQ for additional information.

Similar Papers

Loading similar papers…