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...
Main Authors: | , |
---|---|
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 |
id |
ndltd-UPSALLA1-oai-DiVA.org-mdh-16444 |
---|---|
record_format |
oai_dc |
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 |
_version_ |
1716528229769019392 |