Publications:A general framework for designing a fuzzy rule-based classifier
Property "Publisher" has a restricted application area and cannot be used as annotation property by a user. Property "Author" has a restricted application area and cannot be used as annotation property by a user. Property "Author" has a restricted application area and cannot be used as annotation property by a user. Property "Author" has a restricted application area and cannot be used as annotation property by a user. Property "Author" has a restricted application area and cannot be used as annotation property by a user.
| Title | A general framework for designing a fuzzy rule-based classifier |
|---|---|
| Author | |
| Year | 2011 |
| PublicationType | Journal Paper |
| Journal | Knowledge and Information Systems |
| HostPublication | |
| Conference | |
| DOI | http://dx.doi.org/10.1007/s10115-010-0340-x |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:352177 |
| Abstract | This paper presents a general framework for designing a fuzzyrule-based classifier. Structure and parameters of the classifierare evolved through a two-stage genetic search. To reduce the searchspace, the classifier structure is constrained by a tree createdusing the evolving SOM tree algorithm. Salient input variables arespecific for each fuzzy rule and are found during the genetic searchprocess. It is shown through computer simulations of four real worldproblems that a large number of rules and input variables can beeliminated from the model without deteriorating the classificationaccuracy. By contrast, the classification accuracy of unseen data isincreased due to the elimination.This paper presents a general framework for designing a fuzzyrule-based classifier. Structure and parameters of the classifierare evolved through a two-stage genetic search. To reduce the searchspace, the classifier structure is constrained by a tree createdusing the evolving SOM tree algorithm. Salient input variables arespecific for each fuzzy rule and are found during the genetic searchprocess. It is shown through computer simulations of four real worldproblems that a large number of rules and input variables can beeliminated from the model without deteriorating the classificationaccuracy. By contrast, the classification accuracy of unseen data isincreased due to the elimination. |
