Extraction of quantified fuzzy rules from numerical data
Extraction of quantified fuzzy rules from numerical data
复制标题
从数值数据中提取量化的模糊规则
DOI：
10.1109/fuzzy.2000.839199
复制
发表时间：
2000
期刊：
影响因子：
--
通讯作者：
H. Tamura
中科院分区：
文献类型：
--
作者：
M. Umano;Takahiro Okada;I. Hatono;H. Tamura
提出了一种从数值型数据中提取量化模糊规则的方法。这种类型的模糊规则的一个例子是“属性A为大的大多数数据在属性B中为小”，其中“大”和“小”分别是属性A和B的模糊集，并且“大多数”作为模糊量词。为了选择模糊规则的属性中的模糊集合的组合，我们使用基于模糊ID3的方法来生成指定类的模糊决策树。从每棵树，我们提取一个量化的模糊规则从根到类节点的路径，通过评估其可理解性和信息量。我们将该方法应用于Fisher（1936）的虹膜分类问题和油中含气的诊断数据。
We propose a method to extract quantified fuzzy rules from numerical data. An example of this type of fuzzy rule is "Most data whose attribute A is large are small in the attribute B", where the "large" and "small" are fuzzy sets of attributes A and B, respectively, and "most" as a fuzzy quantifier. For selecting a combination of fuzzy sets in attributes for fuzzy rules, we use a fuzzy ID3-based method to generate a fuzzy decision tree for a specified class. From each tree, we extract a quantified fuzzy rule from a path of the root to a class node by evaluating its understandability and informativeness. We apply the method to Iris classification problem by Fisher (1936) and diagnosis data by gas in oil.