DocumentCode
3026547
Title
Using symbolic data in neuro-fuzzy classification
Author
Nauck, Detlef
Author_Institution
Fac. of Comput. Sci., Magdeburg Univ., Germany
fYear
1999
fDate
36342
Firstpage
536
Lastpage
540
Abstract
In real world data sets, we often have to deal with different kinds of variables. The data can be for example categorical or metric. Data mining methods can often deal with only one kind of data. Even when fuzzy systems are applied-which are not dependent on the scales of variables-usually only metric data is considered. We propose a learning algorithm that creates mixed fuzzy rules-fuzzy rules that use categorical and metric variables
Keywords
data handling; data mining; fuzzy neural nets; fuzzy set theory; learning (artificial intelligence); pattern classification; categorical variables; data mining methods; fuzzy systems; learning algorithm; metric data; metric variables; mixed fuzzy rules; neuro-fuzzy classification; real world data sets; symbolic data; Artificial neural networks; Bayesian methods; Computer science; Data mining; Decision trees; Fuzzy sets; Fuzzy systems; Pattern analysis; Software systems; World Wide Web;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Information Processing Society, 1999. NAFIPS. 18th International Conference of the North American
Conference_Location
New York, NY
Print_ISBN
0-7803-5211-4
Type
conf
DOI
10.1109/NAFIPS.1999.781751
Filename
781751
Link To Document