Location of Repository

Swarm Intelgence Based Rough Set Reduction Scheme for Support Vector Machines

By Ajith Abraham, Senior Member Ieee and Hongbo Liu

Abstract

Abstract — This paper proposes a rough set reduction scheme for Support Vector Machine (SVM). In the proposed scheme, SVM is used for the classification task based on the significance of each feature vector, while rough set is applied to improve feature selection and data reduction. Particle Swarm Optimization (PSO) is used to optimize the rough set feature reduction. The feature vectors are constructed to obtain classification results more effectively. We applied the new approach to classify the brain cognitive state data sets from a cognitive Functional Magnetic Resonance Imaging (fMRI) experiment, in which the subjects perform the task of discerning the orientation of symbols. Empirical results indicate that by using the proposed hybrid scheme it is feasible to achieve either single or multiple subject cognitive state classification more efficiently. I

Year: 2010
OAI identifier: oai:CiteSeerX.psu:10.1.1.160.8118
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://citeseerx.ist.psu.edu/v... (external link)
  • http://www.softcomputing.net/i... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.