DocumentCode
2375192
Title
A fast metric approach to feature subset selection
Author
Chan, Tony Y T
Author_Institution
Aizu Univ., Fukushima, Japan
Volume
2
fYear
1998
fDate
25-27 Aug 1998
Firstpage
733
Abstract
A simple approach to feature subset selection is proposed. During the training stage, the method selects the features that simultaneously minimize the within-class distance and maximize the between-class distance. Experiments performed on the Iris Plants Database and the Pima Indians Diabetes Database show that the approach is practical because it is fast and yet the correct classification rates are competitive
Keywords
data handling; factographic databases; learning (artificial intelligence); pattern classification; Iris Plants Database; Pima Indians Diabetes Database; between-class distance; classification rates; fast metric approach; feature subset selection; training stage; within-class distance; Biological cells; Diabetes; Euclidean distance; Fuzzy neural networks; Iris; Nearest neighbor searches; Neural networks; Probability; Scattering; Spatial databases;
fLanguage
English
Publisher
ieee
Conference_Titel
Euromicro Conference, 1998. Proceedings. 24th
Conference_Location
Vasteras
ISSN
1089-6503
Print_ISBN
0-8186-8646-4
Type
conf
DOI
10.1109/EURMIC.1998.708095
Filename
708095
Link To Document