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

具有数字和符号属性的数据解释规则获取系统的实现

• 批准号: 12680393
• 负责人: UMANO Motohide
• 金额: $ 1.22万
• 依托单位: Osaka Prefecture University
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 2000
• 资助国家: 日本
• 起止时间: 2000 至 2002
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-12680393/
• 关键词: expiatory-rule acquisition; fuzzy rule acquisition; numeric and symbolic attributes; fuzzy decison tree; data mining; fuzzy logic; 知識獲得; ファジィ知識獲得

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.

我们提出了一种方法来获取解释性模糊规则的数据集的数字和符号属性。获取的模糊规则的例子如下：{sex = male}{age = young}的大部分数据是{class = A}，覆盖率为0.91 {sex = female}{height = middle}的几乎所有数据都是{class = B}，覆盖率为0.73，其中“sex”和“class”是符号属性，“age”和“height”是数值属性，“young”和“middle”分别是属性“age”和“height”的模糊集，“most”是比例上的模糊量词。我们使用一个模糊ID 3为基础的算法来生成一个模糊决策树为指定的类。从一个决策树，我们提取一条模糊知识的路径的根到一个类节点，通过评估其可理解性（节点的数量）和信息性（覆盖指定的数据）。我们已经实现了基于该方法的自动规则获取系统。

项目成果

M.Umano, Y.Matsumoto, et al.: "Learning by Switching Generation and Reasoning Methods in Several Knowledge Representations"Eleventh IEEE International Conference on Fuzzy Systems. 809-814 (2002)

M.Umano、Y.Matsumoto 等人：“通过几种知识表示中的切换生成和推理方法进行学习”第十一届 IEEE 模糊系统国际会议。

林, 前田: "TAM Networkによるファジィルール獲得"阪南大学情報科学研究. Vol.15. 22-33 (2002)

Hayashi, Maeda：“TAM 网络的模糊规则获取”汉南大学信息科学研究第 15 卷（2002 年）。

I. Hayashi and J.R. Williamson: "Acquisition of Fuzzy Knowledge from Topographic Mixture Networks with Attentional Feedback"International Joint Conference on Neural Networks (Washington DC, USA, July 15-19, 2001). 1386-1391 (2001)

I. Hayashi 和 J.R. Williamson：“通过注意反馈从地形混合网络获取模糊知识”神经网络国际联合会议（美国华盛顿，2001 年 7 月 15-19 日）。

J.Deng, M.Umano, et al.: "Several Results on Dc Morgan Algebra and Kleene Algebra of Fuzzy Logic"Tenth IEEE International Conference on Fuzzy Systems. (2001)

J.Deng、M.Umano 等人：“模糊逻辑的 DC 摩根代数和 Kleene 代数的几个结果”第十届 IEEE 国际模糊系统会议。

M.Okada, E.Atlam, et al.: "A Method to Estimate the Trend of Words by Using a Decision Tree"in Knowledge-Based Intelligent Information Engineering System and Allied Technologies. 372-376 (2001)

M.Okada，E.Atlam，等人：“一种使用决策树估计词语趋势的方法”，《基于知识的智能信息工程系统及相关技术》。