DocumentCode :
3628994
Title :
Ensemble of rough-neuro-fuzzy systems for classification with missing features
Author :
Marcin Korytkowski;Robert Nowicki;Rafal Scherer;Leszek Rutkowski
Author_Institution :
Department of Computer Engineering, Cz?stochowa University of Technology, al. Armh Krajowej 36, 42-200, Poland
fYear :
2008
fDate :
6/1/2008 12:00:00 AM
Firstpage :
1745
Lastpage :
1750
Abstract :
Most methods constituting the soft computing concept can not handle data with missing or unknown features. Neural networks are able to perfectly fit to data and fuzzy logic systems use interpretable knowledge. To achieve better accuracy learning systems can be combined into larger ensembles. In this paper we combine logical neuro-fuzzy systems into the AdaBoost ensemble and extract fuzzy rules from the ensemble. The rules are used in rough-neuro-fuzzy classifier which can operate on data with missing values. The rough systems perform very well on these rules which was illustrated on a well known benchmark. The features were being removed to check the performance on incomplete data sets.
Keywords :
"Classification algorithms","Approximation methods","Support vector machine classification","Fuzzy sets","Boosting","Equations","Artificial neural networks"
Publisher :
ieee
Conference_Titel :
Fuzzy Systems, 2008. FUZZ-IEEE 2008. (IEEE World Congress on Computational Intelligence). IEEE International Conference on
ISSN :
1098-7584
Print_ISBN :
978-1-4244-1818-3
Type :
conf
DOI :
10.1109/FUZZY.2008.4630606
Filename :
4630606
Link To Document :
بازگشت