基于定性互信息的定性概率网歧义性约简

针对符号传播算法在符号相反的两条平行路径上进行推理时常常产生歧义性，提出一种基于定性互信息的歧义性约简方法。首先，给出定性互信息的严格定义。然后，提出基于定性互信息影响强度的定性概率网，进一步区分影响强度，并证明具有强度的定性影响的对称性、传递性和复合性。最后在Antibiotics数据集上，通过与已有方法推理结果的对比实验，验证该歧义性约简方法的正确性和高效性。理论分析和实验结果表明，基于定性互信息的定性概率网既保留定性推理的简明性，又能够有效约简定性推理的歧义性。

Ambiguity Reduction Based on Qualitative Mutual Information in Qualitative Probabilistic Networks

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.