Data dimension reduction using rough sets for support vector classifier

Authors:
Genting Yan;Guangfu Ma;Liangkuan Zhu
Affiliations:
Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, P.R. China;Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, P.R. China;Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, P.R. China
Venue:
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
Year:
2006

Citing 3
Cited 0

The nature of statistical learning theory

The nature of statistical learning theory
Rough set methods in feature selection and recognition

Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
A comparison of methods for multiclass support vector machines

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes an application of rough sets as a data preprocessing front end for support vector classifier (SVC). A novel multi-class support vector classification strategy based on binary tree is also presented. The binary tree extends the pairwise discrimination capability of the SVC to the multi-class case naturally. Experimental results on benchmark datasets show that proposed method can reduce computation complexity without decreasing classification accuracy compare to SVC without data preprocessing