Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/19039
Title: Improving kernel incapability by equivalent probability in flexible naïve Bayesian
Authors: Liu, JNK
He, YL
Wang, XZ
Keywords: Gaussian kernel
Discontinuous kernel
Equivalent probability
Flexible naïve Bayesian
Kernel incapability
Issue Date: 2012
Publisher: IEEE
Source: 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 10-15 June 2012, Brisbane, QLD, p. 1-8 How to cite?
Abstract: In flexible Naive Bayesian (FNB), the excellent qualities of Gaussian kernel have been demonstrated by the theoretical analyses and experimental comparisons with normal Naive Bayesian(NNB). There are also several types of kernel functions commonly used for probability density estimation, i.e., uniform, triangular, epanechnikov, biweight, triweight and cosine. We call them discontinuous kernels. In this paper, we verify the feasibility and efficiency of applying these alternative kernels in FNB. Our works mainly focus on three aspects: firstly, we give the application conditions of these kernels for the given domain data by analyzing the structural difference between the discontinuous kernel and Gaussian kernel; secondly, the equivalent probability is proposed to improve the capabilities of discontinuous kernels when such problem of kernel incapability occurs; finally, we carry out the experimental demonstration of our proposed method based on 15 UCI datasets. The results show that the discontinuous kernels can obtain better classification accuracies with the help of equivalent probabilities.
URI: http://hdl.handle.net/10397/19039
ISBN: 978-1-4673-1507-4
978-1-4673-1505-0 (E-ISBN)
ISSN: 1098-7584
DOI: 10.1109/FUZZ-IEEE.2012.6250811
Appears in Collections:Conference Paper

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