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Proceedings Paper

Fuzzy Bayesian network classifier for extraction of rocky desertification
Author(s): Shui-ming Li; Ning Shu; Jian-bin Tao; Yin-qiao Zhang
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Paper Abstract

Aiming at the complexity and uncertainty in the interpretation of rocky desertification from multi-source data, this paper promotes fuzzy Bayesian network embedded Gaussian mixture model(GMM) for extracting rocky desertification information.This model make a fuzzy quantization for continuous variable through GMM, use the convex function of multiple Gaussion density functions to fit the "true" distribution of the data better, and avoid variable's discretization in traditional Bayesian network. All nodes's parameter are then integrated utilizing naïve Bayesian network. Experiments indicate that this model have high accuracy than hybrid Bayesian network and with research value in data mining of multi-source data.

Paper Details

Date Published: 15 October 2009
PDF: 8 pages
Proc. SPIE 7492, International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining, 74921V (15 October 2009); doi: 10.1117/12.838275
Show Author Affiliations
Shui-ming Li, Wuhan Univ. (China)
Guangxi Bureau of Geology and Mineral Prospecting and Exploitation (China)
Ning Shu, Wuhan Univ. (China)
Jian-bin Tao, Wuhan Univ. (China)
Yin-qiao Zhang, Guangxi Remote Sensing Ctr. (China)


Published in SPIE Proceedings Vol. 7492:
International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining
Yaolin Liu; Xinming Tang, Editor(s)

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