Publications:A framework for designing a fuzzy rule-based classifier
From ISLAB/CAISR
| Title | A framework for designing a fuzzy rule-based classifier |
|---|---|
| Author | Jonas Guzaitis and Antanas Verikas and Adas Gelzinis and Marija Bacauskiene |
| Year | 2009 |
| PublicationType | Conference Paper |
| Journal | |
| HostPublication | Algorithmic Decision Theory : Proceedings of the 1st International Conference, ADT 2009, Venice, Italy, October 2009 |
| Conference | ADT2009, 1st International Conference on Algorithmic Decision Theory, Venice, Italy, October 2009 |
| DOI | http://dx.doi.org/10.1007/978-3-642-04428-1_38 |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:236181 |
| Abstract | This paper is concerned with a general framework for designing afuzzy rule-based classifier. Structure and parameters of theclassifier are evolved through a two-stage genetic search. Theclassifier structure is constrained by a tree created using theevolving SOM tree algorithm. Salient input variables are specificfor each fuzzy rule and are found during the genetic search process.It is shown through computer simulations of four real world problemsthat a large number of rules and input variables can be eliminatedfrom the model without deteriorating the classification accuracy. |
