Qualitative probabilistic networks with reduced ambiguities |
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Authors: | Kun Yue Yu Yao Jin Li Wei-Yi Liu |
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Affiliation: | (1) Samsung India Electronics Pvt. Ltd, Logic Infotech Park, D-5, Sector-59, Noida, 201305, Uttar Pradesh, India |
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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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