一种改进的基于模糊案例推理方法及其在分类中的应用

针对基于特征权值的相似性模型在相似案例搜索上的局限性，通过汲取模糊规则在捕获领域知识上的有效性和灵活性，提出了一种改进的基于模糊案例推理方法。首先通过确定特征变量取值区间及对特征区间的模糊划分，直接从数据中学习规则；然后计算启动强度并合并规则得到分类器；最后通过计算类别强度，实现对未知案例的分类。3组UCI标准数据集上的实验结果表明，此方法不仅学习时间短，而且可以利用更少的样本获得更好的分类效果。

An Improved Fuzzy Case Based Reasoning Method and Its Application on Classification Problem

To tackle the limitation of feature weights based similarity model in searching similar cases, this paper proposes an improved fuzzy case based reasoning method by utilizing the features of fuzzy rules on capturing the domain knowledge. At first, by deciding the value space of feature variable and dividing the fuzzy regions, this proposed method directly obtains the rules from data. And then, by calculating the firing strength and combining the rules, the classifier can be obtained. Finally, the classification on unsolved cases is established by calculating the classification strength. It is shown from the experimental results on three standard data sets of UCI that the proposed method not only has short learning time, but also can achieve better classification performance using less cases.