By Topic

Multi-label Classification based on Association Rules with Application to Scene 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
$33 $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

4 Author(s)
Bo Li ; Sch. of Inf. Sci. & Eng., Central South Univ., Changsha ; Hong Li ; Min Wu ; Ping Li

In this paper, a multi-label classification based on association rules is proposed. To deal with multiple class labels problem which is hard to settle by existing methods, this algorithm decomposes multi-label data to mine single-label rules, then combines labels with the same attributes to generate multi-label rules. It extracts partial dataset features to build the initial classifier through assembling, and conducts classification prediction by assembling the classifiers. Thus, the computational complexity caused by the high dimensional attributes decreases while the performance and efficiency increases. Then, the multi-label classification algorithm based on association rules which achieve good performance in an application to scene classification.

Published in:

Young Computer Scientists, 2008. ICYCS 2008. The 9th International Conference for

Date of Conference:

18-21 Nov. 2008