By Topic

A Fuzzy Support Vector Machine for Imbalanced Data Classification

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Xiaohong Fan ; Henan Univ. of Urban Constr., Pingdingshan, China ; Zongyao He

The usual fuzzy support vector machines are often affected by the number and distribution of data samples. In order to solve the existing problems, a fuzzy membership is proposed and then a new fuzzy support vector machine was constructed, which is suitable for imbalanced number and distribution data sets. The results show that for welding defects data set welding1, the proposed algorithm under different parameters is superior to the traditional algorithms of SVM and FSVM, whose classification error rate and bias are lower and less affected by parameters; for usual data sets sonar, diabetes, parkinsons, the proposed algorithm has better performances on the classification balance and stability, and its training time is acceptable, which shows this algorithm has good versatility.

Published in:

Optoelectronics and Image Processing (ICOIP), 2010 International Conference on  (Volume:1 )

Date of Conference:

11-12 Nov. 2010