DocumentCode
1742974
Title
Validation of relative feature importance using a natural data set
Author
Holz, H.J. ; Loew, M.H.
Author_Institution
Dept. of Electr. Eng. & Comput. Sci., George Washington Univ., Washington, DC, USA
Volume
2
fYear
2000
fDate
2000
Firstpage
414
Abstract
Feature analysis for classification is based on the discriminately power of features. In our previous research (1997), we presented a method for measuring the non-parametric discriminatory power of features, called relative feature importance (RFI). RFI has been shown to correctly rank features for a variety of artificial data sets. In this research, we validate RFI on natural data using a multiclass natural data set
Keywords
feature extraction; pattern classification; feature extraction; multiclass problem; natural data set; pattern classification; relative feature importance; Costs; Feature extraction; Medical diagnosis; Pathology; Power engineering and energy; Power engineering computing; Power measurement; Radiofrequency interference; Scattering; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 2000. Proceedings. 15th International Conference on
Conference_Location
Barcelona
ISSN
1051-4651
Print_ISBN
0-7695-0750-6
Type
conf
DOI
10.1109/ICPR.2000.906100
Filename
906100
Link To Document