Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/12177
Title: A comparative study among different kernel functions in flexible naïve Bayesian classification
Authors: Liu, JNK
He, Y
Wang, XZ
Hu, YX
Keywords: AUC
CLL
Gaussian
Naïve Bayesian classifier
Biweight
Cosine
Density estimation
Epanechnikov
Triangular
Triweight
Uniform
Issue Date: 2011
Publisher: IEEE
Source: 2011 International Conference on Machine Learning and Cybernetics (ICMLC), 10-13 July 2011, Guilin, p. 638-643 How to cite?
Journal: 2011 International Conference on Machine Learning and Cybernetics (ICMLC), 10-13 July 2011, Guilin 
Abstract: When determining the class of the unknown example by using naïve Bayesian classifier, we need to estimate the class conditional probabilities for the continuous attributes. In flexible Bayesian classifier, the Gaussian kernel function is frequently used for classification task under the framework of Parzen window method. In this paper, the other six kernel functions (uniform, triangular, epanechnikov, biweight, triweight and cosine) are introduced in the flexible naïve Bayesian. The performances of these seven kernels are compared in 30 UCI datasets. The experimental comparisons are carried out according to the following three aspects: the classification accuracy, ranking performance and the class probability estimation. The latter two are measured by the area under the ROC curve (AUC) and the conditional log likelihood (CLL). The related kernels are compared via two-tailed t-test with a 95 percent confidence level and the Friedman's test using the 0.05 critical level. The experimental results show that the most commonly used Gaussian kernel can not achieve the best classification accuracy and AUC. However, on the CLL, the Gaussian kernel is statistically significantly better than the other six kernels. Finally, the corresponding analyses are given based on the experimental results.
URI: http://hdl.handle.net/10397/12177
ISBN: 978-1-4577-0305-8
ISSN: 2160-133X
DOI: 10.1109/ICMLC.2011.6016813
Appears in Collections:Conference Paper

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