符号属性值的相似度学习及属性重要性研究-应用数学专业论文.docxVIP

I I 摘 要 摘 要 在基于案例推理的分类系统（即 CBR 分类器）中，属性间的相似度对分类和决策结 果起着决定性的作用。而属性的相似性度量又依赖于每个属性值之间的相似度计算。本 文研究的是符号属性值之间的相似度，这里考虑的符号值属性是其属性值完全无序的一 类属性，例如属性“颜色”，其取值为“红”，“黄”，“蓝”。大部分研究认为这类属性值 之间的相似度只能取 0 或 1，这种处理方法会导致信息的丢失。已有的工作将这类属性 值的相似度从{0,1}扩展到了[0,1]区间，并用遗传算法进行了学习。但当属性的数量及 值域较大时，遗传算法的收敛速度明显变慢，且分类精度受到影响。基于此考虑，本文 提出一种基于粒子群的相似度学习算法来获得符号属性值的相似度，通过实验证明，基 于粒子群的算法比遗传算法收敛速度快，精度高。 此外，本研究进一步指出，通过学习获得的属性值相似度可以粗略反应属性本身的 重要程度，并给出了属性的重要性度量。实验验证了这种度量的可行性。 最后，基于粗糙集理论中相对正域的概念，本文还提出了一种判断数据集的属性之 间有无交互作用的方法。 关键词 符号值属性 相似度 粒子群算法 属性重要性度量 粗集约简 II II Abstract Abstract When studying cased-based reasoning classifiers (i.e. CBR classifiers), similarity between features occupies a decisive role for the results of classification and decision-making. Similarity measure between features depends on calculation of similarity between each feature value. This paper learns similarity between values of symbolic features. The symbolic features considered here have completely unordered values, such as for the feature “color”, values are “red”, “yellow” and “blue”. Most researchers considers similarities between these feature values can only be either 0 or 1, this approach will lead to loss of information. Existing work has improved these values from {0, 1} to [0, 1] and has presented A GA-based approach for learning similarity measure of symbolic feature values. However, when the number of feature values and features become larger, the convergence speed based GA obviously slows down, and the accuracy of classification may be also affected. Considered this reason, this paper proposed a PSO-based method to get similarity measure of symbolic features. The results of the experiments show that, the convergence speed based on PSO algorithm is much faster than based on GA algorithm and the accuracy is also improved. In addition, this paper has futher indicated that similarities of feature values we learnt can roughly reflect feature importance and proposed feature importance measure. Experiments proved the feasibility of this