Evidence theory is a powerful tool to deal with uncertain problems.However, the evidence is usually obtained from experts with a high degree of subjectivity, and the importance of different evidences combined is equivalent.A novel method of evidence acquirement and combination based on rough set is proposed.Confidence degrees of evidence are used to calculate approximate conditional probability assignments.Evidence weights are calculated according to the attribute significances and support degrees of evidence.Decisions are gained by using combinational rule to integrate approximate conditional probability assignments.The results show the validity of this method.%证据理论是处理不确定性问题的有效工具,但是其证据往往来源于专家,带有很大的主观性,且合成证据的重要性无优劣之分.提出了一种基于粗糙集的证据获取与合成方法.利用证据信任度计算近似条件概率分配,根据属性重要度和证据支持度计算权重,用合成公式对近似条件概率分配进行合成得到决策.实例结果表明了该方法的有效性.
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