A data preprocessing algorithm based on rough set for SVM classifier

Author

Zhiqi Huang ; Jun Guo

Author_Institution

Comput. Center, East China Normal Univ., Shanghai, China

fYear

2013

fDate

Nov. 29 2013-Dec. 1 2013

Firstpage

441

Lastpage

444

Abstract

Support vector machine (SVM) is now widely applied in various areas for its excellent performances. For a data set, usually we use normalization method to deal with the features. However, in many cases, the value of each feature is different. Thus, SVM can´t work very well. In this paper, we propose a preprocessing algorithm based on rough set (RS) theory to give different weights on each feature, which can well reflect the value of each feature. The experimental results on real data show that the proposed approach can achieve a fairly improvement of classification accuracy.

Keywords

data handling; feature extraction; pattern classification; rough set theory; support vector machines; SVM classifier; classification accuracy; data preprocessing algorithm; feature weights; normalization method; rough set theory; support vector machine; Accuracy; Algorithm design and analysis; Classification algorithms; Conferences; Kernel; Support vector machines; Training; SVM; feature; preprocessing algorithm; rough set;

fLanguage

English

Publisher

ieee

Conference_Titel

Control System, Computing and Engineering (ICCSCE), 2013 IEEE International Conference on

Conference_Location

Mindeb

Print_ISBN

978-1-4799-1506-4

Type

conf

DOI

10.1109/ICCSCE.2013.6720005

Filename

6720005