A Comparative Study of Bandwidth Choice in Kernel Density Estimation for Naive Bayesian Classification

@inproceedings{Liu2009ACS,
  title={A Comparative Study of Bandwidth Choice in Kernel Density Estimation for Naive Bayesian Classification},
  author={Bin Liu and Ying Yang and Geoffrey I. Webb and Janice R. Boughton},
  booktitle={PAKDD},
  year={2009}
}
Kernel density estimation (KDE) is an important method in nonparametric learning. While KDE has been studied extensively in the context of accuracy of density estimation, it has not been studied extensively in the context of classification. This paper studies nine bandwidth selection schemes for kernel density estimation in Naive Bayesian classification context, using 52 machine learning benchmark datasets. The contributions of this paper are threefold. First, it shows that some commonly used… CONTINUE READING

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