DocumentCode
2753172
Title
Classification using small fuzzy biological data sets
Author
Diederich, Jim ; Fortuner, Renaud
Author_Institution
Dept. of Math., California Univ., Davis, CA, USA
Volume
2
fYear
1998
fDate
4-9 May 1998
Firstpage
1429
Abstract
This paper examines fuzzy methods to identify populations from well-described closely related species. Real nematological data is used to assess the potential and limitations of several methods including one that is introduced to handle large numbers of attributes. Both crisp and fuzzy representations of the data are considered
Keywords
biology computing; data structures; fuzzy set theory; fuzzy systems; knowledge based systems; learning systems; pattern classification; biological data sets; fuzzy data representations; fuzzy rule based system; fuzzy set theory; nematological data; pattern classification; related species; Doped fiber amplifiers; Functional analysis; Fuzzy sets; Learning systems; Mathematics; Measurement standards; Size measurement; Tail; Testing; Training data;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on
Conference_Location
Anchorage, AK
ISSN
1098-7584
Print_ISBN
0-7803-4863-X
Type
conf
DOI
10.1109/FUZZY.1998.686329
Filename
686329
Link To Document