Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Generating Fuzzy Rules For Case-based Classification
Mälardalen University, School of Innovation, Design and Engineering.
Mälardalen University, School of Innovation, Design and Engineering.
2012 (English)Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

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.

Place, publisher, year, edition, pages
2012. , 40 p.
Keyword [en]
case-based reasoning, classification, fuzzy rules, learning
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:mdh:diva-16444OAI: oai:DiVA.org:mdh-16444DiVA: diva2:574928
Presentation
2012-05-30, 16:30 (English)
Uppsok
Technology
Supervisors
Examiners
Available from: 2012-12-17 Created: 2012-12-07 Last updated: 2012-12-17Bibliographically approved

Open Access in DiVA

Thesis_Ma_Zhang(1089 kB)124 downloads
File information
File name FULLTEXT01.pdfFile size 1089 kBChecksum SHA-512
5a6d7f1884320ed70c36613d0eacddd761714b92ead11785c010f753010ab46de5b26604905b6c0dffdcf87984a0fb4d449d705b4991de3a7111e3fd669b53a4
Type fulltextMimetype application/pdf

By organisation
School of Innovation, Design and Engineering
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar
Total: 124 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 148 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf