Structural learning of fuzzy rules from noised examples

Author

Gonzalez, Antonio ; Perez, Raul

Author_Institution

Dept. de Ciencias de la Comput. e Inteligencia Artificial, Granada Univ., Spain

Volume

3

fYear

1995

fDate

20-24 Mar 1995

Firstpage

1323

Abstract

Inductive learning algorithms obtain the knowledge of a system from a set of examples. One of the most difficult problems in machine learning is to obtain the structure of this knowledge. We propose an algorithm which is able to manage fuzzy information and to learn the structure of the rules that represent the system. The algorithm gives a reasonable small set of fuzzy rules that represent the original set of examples

Keywords

fuzzy logic; fuzzy set theory; knowledge acquisition; knowledge based systems; knowledge representation; learning by example; uncertainty handling; fuzzy information; fuzzy rules; inductive learning algorithms; knowledge acquisition; knowledge representation; machine learning; structural learning; Electronic mail; Fuzzy sets; Fuzzy systems; Knowledge acquisition; Knowledge based systems; Machine learning; Machine learning algorithms; Tiles;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 1995. International Joint Conference of the Fourth IEEE International Conference on Fuzzy Systems and The Second International Fuzzy Engineering Symposium., Proceedings of 1995 IEEE Int

Conference_Location

Yokohama

Print_ISBN

0-7803-2461-7

Type

conf

DOI

10.1109/FUZZY.1995.409853

Filename

409853