Name
Abstract | ||
|---|---|---|
Feature analysis for classification is based on the discriminatory power of features. In previous research, we have presented a metric called relative feature importance (RFI) for measuring the non-parametric discriminatory power (NPDP) of features. RFI has been shown to correctly rank features for a variety of artificial data sets. In this work, we validate RFI on natural data, using several natural data sets. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1016/S0167-8655(01)00170-2 | Pattern Recognition Letters |
Keywords | Field | DocType |
feature analysis,natural data set,feature selection,discriminatory power,natural data,classifier-independent,artificial data set,non-parametric discriminatory power,relative feature importance,previous research,non-parametric,non parametric | Data mining,Data set,Feature selection,Pattern recognition,Computer science,Nonparametric statistics,Artificial intelligence,Pattern recognition (psychology) | Journal |
Volume | Issue | ISSN |
23 | 4 | Pattern Recognition Letters |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hilary J. Holz | 1 | 24 | 6.24 |
| Murray H. Loew | 2 | 151 | 47.53 |