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Qualitative probabilistic networks with reduced ambiguities

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Abstract

A Qualitative probabilistic network (QPN) is the qualitative abstraction of a Bayesian network that encodes variables and the qualitative influences between them. In order to make QPNs be practical for real-world representation and inference of uncertain knowledge, it is desirable to reduce ambiguities in general QPNs, including unknown qualitative influences and inference conflicts. In this paper, we first extend the traditional definition of qualitative influences by adopting the probabilistic threshold. In addition, we introduce probabilistic-rough-set-based weights to the qualitative influences. The enhanced network so obtained, called EQPN, is constructed from sample data. Finally, to achieve conflict-free EQPN inferences, we resolve the trade-offs by addressing the symmetry, transitivity and composition properties. Preliminary experiments verify the correctness and feasibility of our methods.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, No. 2 North Cuihu Road, Kunming, Yunnan, People’s Republic of China

    Kun Yue, Yu Yao & Wei-Yi Liu

  2. Department of Software Engineering, School of Software, Yunnan University, No. 2 North Cuihu Road, Kunming, Yunnan, People’s Republic of China

    Jin Li

Authors
  1. Kun Yue
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  2. Yu Yao
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  3. Jin Li
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  4. Wei-Yi Liu
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Corresponding author

Correspondence to Kun Yue.

Additional information

This work was supported by the National Natural Science Foundation of China (No. 60763007), the Natural Science Foundation of Yunnan Province (No. 2008CD083), and the Research Foundation of the Educational Department of Yunan Province (No. 08Y0023).

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Yue, K., Yao, Y., Li, J. et al. Qualitative probabilistic networks with reduced ambiguities. Appl Intell 33, 159–178 (2010). https://doi.org/10.1007/s10489-008-0156-5

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  • DOI: https://doi.org/10.1007/s10489-008-0156-5

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