Szczegóły publikacji
Opis bibliograficzny
Boosting the Fisher Linear Discriminant with random feature subsets / Tomasz ARODŹ // W: Computer Recognition Systems : proceedings of the 4th international conference on Computer Recognition Systems CORES'05 : [May 22–25, 2005, Rydzyna, Poland] / eds. Marek Kurzyński [et al.]. — Berlin, Heidelberg : Springer-Verlag, 2005. — (Advances in Soft Computing ; ISSN 1615-3871 ; vol. 30). — ISBN: 978-3-540-25054-8; e-ISBN: 978-3-540-32390-7. — S. 79–86. — Bibliogr. s. 85–86, Summ.
Autor
Dane bibliometryczne
| ID BaDAP | 26793 |
|---|---|
| Data dodania do BaDAP | 2006-03-24 |
| Rok publikacji | 2005 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Czasopismo/seria | Advances in Soft Computing |
Abstract
Boosting increases the recognition accuracy of many types of classifiers. However, studies show that for the Fisher Linear Discriminant (FLD), a simple and widely used classifier, boosting does not lead to a significant increase in accuracy. In this paper, a new method for adapting the FLD into the boosting framework is proposed. This method, the AdaBoost-RandomFeatureSubset-FLD (AB-RFS-FLD), uses a different, randomly chosen subset of features for learning in each boosting round. The new method achieves significantly better accuracy than both single FLD and FLD with boosting, with improvements reaching 6% in some cases. We show that the good performance can be attributed to higher diversity of the individual FLDs, as well as to the better generalization abilities.
