Implementation of Explanatory-Rule Acquisition System from Data with Numeric and Symbolic Attributes

我们提出了一种从具有数字和符号属性的数据集中获取解释性模糊规则的方法。所获得的模糊规则的例子如下：{性别=男性}{年龄=年轻}的大部分数据是覆盖率为0.91的{类别=A}。{性别=女性}{高度=中等}的几乎所有数据都是覆盖率为0.73的{类别=B}，其中，性别和类别是符号属性，而年龄和身高是数值属性，年轻和中等分别是属性的模糊集合，以及比例中的模糊量词。由于真实数据集包括噪声和误差，我们不能将传统的方法应用于各个领域的研究。我们使用一种基于模糊ID3的算法来为指定的类生成模糊决策树。从决策树中，我们通过评估其可理解性(节点数)和信息性(指定数据的覆盖率)来从根到类节点的路径中提取模糊知识。基于该方法，我们实现了一个解释规则获取系统。

We propose a method to acquire explanatory fuzzy rules from a data set with numeric and symbolic attributes. Examples of acquired fuzzy rules are the followings:Most data of {sex = male}{age = young} are {class = A} with coverage 0.91Almost all data of {sex = female}{height = middle} are {class = B} with coverage 0.73where "sex" and "class" are symbolic attributes and "age" and "height" are numeric ones, "young" and "middle" are fuzzy sets of attributes "age" and "height," respectively, and "most" is a fuzzy quantifier in the proportion.Since a real data set includes noise and errors, we can not apply conventional methods studied in a various fields. We use a fuzzy ID3-based algorithm to generate a fuzzy decision tree for a specified class. From a decision tree, we extract a piece of fuzzy knowledge from a path of the root to a class node by evaluating its understandability (the number of nodes) and informativeness (coverage of the specified data). We have implemented a explanatory-rule acquisition system based on the method.