Generating Fuzzy Rules For Case-based Classification

As a technique to solve new problems based on previous successful cases, CBR represents significant prospects for improving the accuracy and effectiveness of unstructured decision-making problems. Similar problems have similar solutions is the main assumption. Utility oriented similarity modeling is...

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Main Authors: Ma, Liangjun, Zhang, Shouchuan
Format: Others
Language:English
Published: Mälardalens högskola, Akademin för innovation, design och teknik 2012
Subjects:
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-16444
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spelling ndltd-UPSALLA1-oai-DiVA.org-mdh-164442013-01-08T13:45:56ZGenerating Fuzzy Rules For Case-based ClassificationengMa, LiangjunZhang, ShouchuanMälardalens högskola, Akademin för innovation, design och teknikMälardalens högskola, Akademin för innovation, design och teknik2012case-based reasoningclassificationfuzzy ruleslearningAs a technique to solve new problems based on previous successful cases, CBR represents significant prospects for improving the accuracy and effectiveness of unstructured decision-making problems. Similar problems have similar solutions is the main assumption. Utility oriented similarity modeling is gradually becoming an important direction for Case-based reasoning research. In this thesis, we propose a new way to represent the utility of case by using fuzzy rules. Our method could be considered as a new way to estimate case utility based on fuzzy rule based reasoning. We use modified WANG’s algorithm to generate a fuzzy if-then rule from a case pair instead of a single case. The fuzzy if-then rules have been identified as a powerful means to capture domain information for case utility approximation than traditional similarity measures based on feature weighting. The reason why we choose the WANG algorithm as the foundation is that it is a simpler and faster algorithm to generate if-then rules from examples. The generated fuzzy rules are utilized as a case matching mechanism to estimate the utility of the cases for a given problem. The given problem will be formed with each case in the case library into pairs which are treated as the inputs of fuzzy rules to determine whether or to which extent a known case is useful to the problem. One case has an estimated utility score to the given problem to help our system to make decision. The experiments on several data sets have showed the superiority of our method over traditional schemes, as well as the feasibility of learning fuzzy if-then rules from a small number of cases while still having good performances. Student thesisinfo:eu-repo/semantics/bachelorThesistexthttp://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-16444application/pdfinfo:eu-repo/semantics/openAccess
collection NDLTD
language English
format Others
sources NDLTD
topic case-based reasoning
classification
fuzzy rules
learning
spellingShingle case-based reasoning
classification
fuzzy rules
learning
Ma, Liangjun
Zhang, Shouchuan
Generating Fuzzy Rules For Case-based Classification
description As a technique to solve new problems based on previous successful cases, CBR represents significant prospects for improving the accuracy and effectiveness of unstructured decision-making problems. Similar problems have similar solutions is the main assumption. Utility oriented similarity modeling is gradually becoming an important direction for Case-based reasoning research. In this thesis, we propose a new way to represent the utility of case by using fuzzy rules. Our method could be considered as a new way to estimate case utility based on fuzzy rule based reasoning. We use modified WANG’s algorithm to generate a fuzzy if-then rule from a case pair instead of a single case. The fuzzy if-then rules have been identified as a powerful means to capture domain information for case utility approximation than traditional similarity measures based on feature weighting. The reason why we choose the WANG algorithm as the foundation is that it is a simpler and faster algorithm to generate if-then rules from examples. The generated fuzzy rules are utilized as a case matching mechanism to estimate the utility of the cases for a given problem. The given problem will be formed with each case in the case library into pairs which are treated as the inputs of fuzzy rules to determine whether or to which extent a known case is useful to the problem. One case has an estimated utility score to the given problem to help our system to make decision. The experiments on several data sets have showed the superiority of our method over traditional schemes, as well as the feasibility of learning fuzzy if-then rules from a small number of cases while still having good performances.
author Ma, Liangjun
Zhang, Shouchuan
author_facet Ma, Liangjun
Zhang, Shouchuan
author_sort Ma, Liangjun
title Generating Fuzzy Rules For Case-based Classification
title_short Generating Fuzzy Rules For Case-based Classification
title_full Generating Fuzzy Rules For Case-based Classification
title_fullStr Generating Fuzzy Rules For Case-based Classification
title_full_unstemmed Generating Fuzzy Rules For Case-based Classification
title_sort generating fuzzy rules for case-based classification
publisher Mälardalens högskola, Akademin för innovation, design och teknik
publishDate 2012
url http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-16444
work_keys_str_mv AT maliangjun generatingfuzzyrulesforcasebasedclassification
AT zhangshouchuan generatingfuzzyrulesforcasebasedclassification
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