DocumentCode :
2264648
Title :
Fuzzy logic extension of nonparametric approach to feature selection and binary decision tree design
Author :
Mirshab, B. ; Anneberg, L.
Author_Institution :
Dept. of Electr. Eng., Lawrence Technol. Univ., Southfield, MI, USA
fYear :
1993
fDate :
16-18 Aug 1993
Firstpage :
828
Abstract :
The binary decision tree for pattern recognition systems based on the selection of a set of features is both capable of high discrimination and is economical. A further extension is proposed which will incorporate fuzzy logic decisions at each node as part of the feature selection process. Each node will return a possibilistic response: “possibly” or “possibly not” as the decision regarding a set p.f. patterns. The advantage to fuzzy decision nodes is that the input to the decision tree may be linguistic in nature and the tree could return a `crisp´ or defuzzified output, which will be a useful result
Keywords :
character recognition; feature extraction; fuzzy logic; image recognition; binary decision tree design; feature selection; fuzzy logic decisions; nonparametric approach; pattern recognition systems; Algorithm design and analysis; Clustering algorithms; Data analysis; Decision trees; Design engineering; Fuzzy logic; Fuzzy systems; Image processing; Pattern recognition; Training data;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Circuits and Systems, 1993., Proceedings of the 36th Midwest Symposium on
Conference_Location :
Detroit, MI
Print_ISBN :
0-7803-1760-2
Type :
conf
DOI :
10.1109/MWSCAS.1993.343197
Filename :
343197
Link To Document :
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