DocumentCode :
2416809
Title :
A Method for the Fuzzification of Categorical Variables
Author :
Jodoin, Etienne ; Reyes, Carlos Andrés Pena ; Sanchez, Eduardo
Author_Institution :
Swiss Fed. Inst. of Technol., Lausanne
fYear :
0
fDate :
0-0 0
Firstpage :
831
Lastpage :
838
Abstract :
Besides the numeric variables which are common in fuzzy modeling, some variables involved in the description of specific behaviors are categorical. Such variables are discrete, have no order a-priori, and most of the time handle a large amount of values (e.g., genes, proteins, countries, religions, etc.). This paper proposes a methodology for the fuzzification of categorical variables which could make part of a larger fuzzy modeling approach. The proposed solution allows to automatically create fuzzy membership functions for nominal categorical variables. We study some parameters so as to better assess their possible effect on the final outcome of the whole fuzzy modeling process.
Keywords :
fuzzy logic; fuzzy systems; categorical variable fuzzification; fuzzy membership function; fuzzy modeling process; fuzzy system; Continents; Databases; Engines; Fuzzy systems; Inference algorithms; Logic; Multidimensional systems; Proteins; Support vector machines; Uncertainty;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems, 2006 IEEE International Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-9488-7
Type :
conf
DOI :
10.1109/FUZZY.2006.1681807
Filename :
1681807
Link To Document :
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