Learning in neuro-fuzzy systems with symbolic attributes and missing values

Author

Nauck, Detlef ; Kruse, Rudolf

Author_Institution

Intelligent Syst. Res. Group, British Telecom, UK

Volume

1

fYear

1999

fDate

1999

Firstpage

142

Abstract

Neuro-fuzzy classification approaches aim at creating fuzzy classification rules from data by using learning techniques derived from neural networks. NEFCLASS is able to learn fuzzy rules and fuzzy sets by simple heuristics. The aim of NEFCLASS is to quickly create interpretable fuzzy classifiers. Most neuro-fuzzy approaches can only deal with numerical attributes and cannot handle missing values. The authors present recent advances in the learning algorithms of NEFCLASS that address those problems

Keywords

data handling; fuzzy neural nets; fuzzy set theory; learning (artificial intelligence); pattern classification; NEFCLASS; fuzzy classification rules; fuzzy sets; interpretable fuzzy classifiers; learning algorithms; learning techniques; missing values; neural networks; neuro-fuzzy approaches; neuro-fuzzy classification approaches; neuro-fuzzy systems; numerical attributes; simple heuristics; symbolic attributes; Computer science; Electronic mail; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Intelligent networks; Intelligent systems; Telecommunications; Training data; World Wide Web;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Information Processing, 1999. Proceedings. ICONIP '99. 6th International Conference on

Conference_Location

Perth, WA

Print_ISBN

0-7803-5871-6

Type

conf

DOI

10.1109/ICONIP.1999.843976

Filename

843976