Ambiguity Reduction Based on Qualitative Mutual Information in Qualitative Probabilistic Networks
L Ya-Li1,2, LIAO Shi-Zhong1
1.School of Computer Science and Technology, Tianjin University, Tianjin 300072 2.School of Information Management, Shanxi University of Finance and Economics, Taiyuan 030031
Abstract To reduce the inference ambiguity in the sign-propagation algorithm, a method is proposed based on qualitative mutual information in qualitative probabilistic networks (QPN). Firstly, the definition of qualitative mutual information is given. Then, an enhanced formalism of qualitative probabilistic networks (EQPN) is presented based on this definition, which can distinguish between strong and weak influences. Thirdly, symmetry, transitivity and parallel composition of qualitative influences in EQPN are analyzed. Finally, the correctness and efficiency of the sign-propagation algorithm in EQPN are verified by experiments on the Antibiotics database. Theoretic analysis and experimental results show that EQPN is qualitative, efficient, and it reduces inference ambiguity correctly.
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2011, Vol. 24 
