案例推理属性权重的分配模型比较研究

A Comparative Study of Attribute Weights Assignment for Case-based Reasoning

案例推理系统中各属性权重的赋值决定了案例之间的相似度大小,进而对推理结果的正确与否产生显著影响.以属性加权K-最近邻相似案例检索为基础,讨论了使用注水原理分配属性权重的机理,并通过建立权重分配的合理性指标,构造拉格朗日函数对权重进行优化求解,得到了收敛的注水分配算法.通过五折交叉的模式分类实验,分别对属性权重的平均分配法、注水分配算法和遗传算法分配法进行了比较研究,案例推理分类结果证明,在引入注水分配算法后,其分类性能得到有效改善.

The attribute weights assignment in case-based reasoning （CBR） system may determine the similarities between cases, and thus it has a significant impact on the correctness of reasoning. To improve the reasoning performance, the water-filling theory is introduced to the attribute weights assignment in this paper. Reasonable indicators of weight distribution are established, an associated Lagrange function is constructed and the weight optimization solution can be achieved. Thereby a convergent water-filling assignment （WFA） algorithm is obtained which can be used in the weighted K-nearest neighbor rule to retrieve similar cases. Classification experiments for comparison between the mean assignment method, WFA method and genetic algorithms for the attribute weights using the 5-fold cross-validation method are conducted. The results show that the classification performance of CBR can be further increased after the attribute weights are assigned by WFA.