A general framework for designing a fuzzy rule-based classifier

作者：Verikas Antanas^*; Guzaitis Jonas; Gelzinis Adas; Bacauskiene Marija

来源：Knowledge and Information Systems, 2011, 29(1): 203-221.

DOI：10.1007/s10115-010-0340-x

• Classifier
• Fuzzy rule
• Genetic algorithm
• Knowledge extraction
• Variable selection
• Evolving SOM tree

This paper presents a general framework for designing a fuzzy rule-based classifier. Structure and parameters of the classifier are evolved through a two-stage genetic search. To reduce the search space, the classifier structure is constrained by a tree created using the evolving SOM tree algorithm. Salient input variables are specific for each fuzzy rule and are found during the genetic search process. It is shown through computer simulations of four real world problems that a large number of rules and input variables can be eliminated from the model without deteriorating the classification accuracy. By contrast, the classification accuracy of unseen data is increased due to the elimination.

• 出版日期2011-10