| Project/Area Number |
12680393
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | Osaka Prefecture University |
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Principal Investigator |
UMANO Motohide Osaka Prefecture University, College of Integrated Arts and Sciences, Professor, 総合科学部, 教授 (10131616)
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| Co-Investigator(Kenkyū-buntansha) |
HAYASHI Isao Hayashi Hannan University, Graduate School of Corporate Information, Professor, 経営情報学部, 教授 (70258078)
OKADA Makoto Osaka Prefecture University, College of Integrated Arts and Sciences, Research Associates, 総合科学部, 助手 (40336813)
宇野 裕之 大阪府立大学, 総合科学部, 講師 (60244670)
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| Project Period (FY) |
2000 – 2002
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| Project Status |
Completed (Fiscal Year 2002)
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| Budget Amount *help |
¥1,900,000 (Direct Cost: ¥1,900,000)
Fiscal Year 2002: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 2001: ¥1,100,000 (Direct Cost: ¥1,100,000)
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| Keywords | 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 |
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| Research Abstract |
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 0.91
Almost all data of {sex = female}{height = middle} are {class = B} with coverage 0.73
where "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.
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