Feature subset selection based on mahalanobis distance: A statistical rough set method / 西安交通大学学报·英文版
Academic Journal of Xi'
;
an Jiaotong University;(4): 14-18, 2008.
Article
Dans Chinois
| WPRIM
| ID: wpr-844842
ABSTRACT
In order to select effective feature subsets for pattern classification, a novel statistics rough set method is presented based on generalized attribute reduction. Unlike classical reduction approaches, the objects in universe of discourse are signs of training sample sets and values of attributes are taken as statistical parameters. The binary relation and discernibility matrix for the reduction are induced by distance function. Furthermore, based on the monotony of the distance function defined by Mahalanobis distance, the effective feature subsets are obtained as generalized attribute reducts. Experiment result shows that the classification performance can be improved by using the selected feature subsets.
Texte intégral:
Disponible
Indice:
WPRIM (Pacifique occidental)
langue:
Chinois
Texte intégral:
Academic Journal of Xi'an Jiaotong University
Année:
2008
Type:
Article
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