Validation of relative feature importance using a natural data set

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...

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Bibliographic Details
Published in:	Proceedings 15th International Conference on Pattern Recognition. ICPR-2000 Vol. 2; pp. 414 - 417 vol.2
Main Authors:	Holz, H.J., Loew, M.H.
Format:	Conference Proceeding
Language:	English
Published:	IEEE 2000
Subjects:	Costs Feature extraction Medical diagnosis Pathology Power engineering and energy Power engineering computing Power measurement Radiofrequency interference Scattering Testing
Online Access:	Get full text
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Summary:	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.
ISBN:	0769507506 9780769507507
ISSN:	1051-4651 2831-7475
DOI:	10.1109/ICPR.2000.906100