Name
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 |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1109/ICPR.2000.906100 | Pattern Recognition, 2000. Proceedings. 15th International Conference |
Keywords | Field | DocType |
feature extraction,pattern classification,feature extraction,multiclass problem,natural data set,pattern classification,relative feature importance | k-nearest neighbors algorithm,Data mining,Data set,Dimensionality reduction,Pattern recognition,Computer science,Feature (computer vision),Feature extraction,Feature (machine learning),Artificial intelligence,Kanade–Lucas–Tomasi feature tracker,Pattern recognition (psychology) | Conference |
Volume | ISSN | ISBN |
2 | 1051-4651 | 0-7695-0750-6 |
Citations | PageRank | References |
1 | 0.36 | 1 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hilary J. Holz | 1 | 24 | 6.24 |
| Murray H. Loew | 2 | 151 | 47.53 |