Cart (Loading....) | Create Account
Close category search window

New Method for Mixed Abnormal Pattern Recognition Using Multi-Class Support Vector Machines

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

1 Author(s)
Deihui Wu ; Key Lab. of Numerical Control of Jiangxi Province, Jiujiang Univ., Jiujiang, China

Control charts is an important tool of statistical quality control (SQC), and the recognition of mixed abnormal pattern exists on the control chart is one of difficulties of on-line intelligent process quality diagnosis. After limitations of control chart-recognizers used in practice were analyzed, a novel intelligent process quality diagnosis method is proposed with a special model of multi-class support vector machine (MSVM). In this model, the binary decision tree is firstly used in recognizing the controlled process sample with upper on-line recognition rapidity. Then five single feature abnormal models are recognized using multi-class SVM classifiers in one-versus-one (OVO) decomposition. Finally, to make full use of the unclassifiable regions existing in the traditional "Max-Wins" voting (MWV) strategy, the capability is realized to recognize mixed abnormal patterns. Numerical results are given to demonstrate that, the proposed method can achieve the better classification ability and resolve the mixed abnormal pattern recognition problem. So, it provides a candidate for the small-batch production process quality online diagnosis and control.

Published in:

Pattern Recognition (CCPR), 2010 Chinese Conference on

Date of Conference:

21-23 Oct. 2010

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.