DocumentCode :
3319770
Title :
Niching genetic feature selection algorithms applied to the design of fuzzy rule-based classification systems
Author :
Aguilera, Jose Joaquin ; Chica, Manuel ; Del Jesus, Maria Jose ; Herrera, Francisco
Author_Institution :
Univ. of Jaen, Jaen
fYear :
2007
fDate :
23-26 July 2007
Firstpage :
1
Lastpage :
6
Abstract :
In the design of fuzzy rule-based classification systems (FRBCSs) a feature selection process which determines the most relevant features is a crucial component in the majority of the classification problems. This simplification process increases the efficiency of the design process, improves the interpretability of the FRBCS obtained and its generalization capacity. Most of the feature selection algorithms provide a set of variables which are adequate for the induction process according to different quality measures. Nevertheless it can be useful for the induction process to determine not only a set of variables but also different set of variables. These sets of variables can be used for the design of a set of FRBCSs which can be combined in a multiclassifler system, improving the prediction capacity increasing its description capacity. In this work, different proposals of niching genetic algorithms for the feature selection process are analyzed. The different sets of features provided by them are used in a multiclassifier system designed by means of a genetic proposal. The experimentation shows the adaptation of this type of genetic algorithms to the FRBCS design.
Keywords :
classification; feature extraction; fuzzy set theory; genetic algorithms; description capacity; feature selection algorithms; fuzzy rule-based classification systems; induction process; multiclassifler system; niching genetic algorithms; prediction capacity; Algorithm design and analysis; Data mining; Databases; Fuzzy reasoning; Fuzzy sets; Fuzzy systems; Genetic algorithms; Knowledge representation; Process design; Proposals;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems Conference, 2007. FUZZ-IEEE 2007. IEEE International
Conference_Location :
London
ISSN :
1098-7584
Print_ISBN :
1-4244-1209-9
Electronic_ISBN :
1098-7584
Type :
conf
DOI :
10.1109/FUZZY.2007.4295638
Filename :
4295638
Link To Document :
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