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 inProceedings 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
LanguageEnglish
Published IEEE 2000
Subjects
Costs
Feature extraction
Medical diagnosis
Pathology
Power engineering and energy
Power engineering computing
Power measurement
Radiofrequency interference
Scattering
Testing
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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