Name
Abstract | ||
|---|---|---|
In ( Holz and Loew 1994a, b ), we presented a metric for use in classifier-independent feature analysis called relative feature importance (RFI). RFI was shown to correctly rank features on a variety of two-class multi-cluster, mixed-distribution problems, including problems that cannot be solved using the marginal distributions of the features. We present here a complete design for RFI, including new results on parameter settings and calculation details determined on two-class problems. We then show that, using the design arising from exploration of two-class problems, RFI extends naturally and successfully to multi-class problems. |
Year | DOI | Venue |
|---|---|---|
1997 | 10.1016/S0167-8655(97)00118-9 | Pattern Recognition Letters |
Keywords | Field | DocType |
feature analysis,discriminatory power,feature selection,ranking,multi-class classifier-independent feature analysis | Data mining,Ranking,Feature selection,Pattern recognition,Artificial intelligence,Classifier (linguistics),Mathematics,Pattern recognition (psychology),Marginal distribution | Journal |
Volume | Issue | ISSN |
18 | 11-13 | Pattern Recognition Letters |
Citations | PageRank | References |
3 | 0.72 | 2 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hilary J. Holz | 1 | 24 | 6.24 |
| Murray H. Loew | 2 | 151 | 47.53 |