By Topic

BS-SVM multi-classification model in the application of consumer goods

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)
Quanhui Jia ; Sch. of Econ. & Manage., Beijing Univ. of Aeronaut. & Astronaut., Beijing, China ; Lieli Liu

Quality and safety of consumer products have drawn wide attention from scholars in related domain, this issue is based on the subject of the quality and safety of consumer goods, in accordance with characteristics of cases, and put forward a hierarchical support vector machine classification algorithm based on the relative separability of the feature space, to solve the low classification performance and high rate of misclassification of the existing algorithms. The weight of Binary Search Tree is the separability of samples, determining the order of categories by a selective set of training samples to construct SVM classifier and the final formation of a binary classification of the larger interval multi-valued SVM classifier tree. Simulation results show that the method has a faster test speed, relatively perfect good classification accuracy and generalization performance.

Published in:

Software Engineering and Service Science (ICSESS), 2011 IEEE 2nd International Conference on

Date of Conference:

15-17 July 2011