By Topic

Knowledge discovery-based multiple classifier fusion: a generalized rough set method

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

3 Author(s)
Liang Sun ; Sch. of Electron. & Inf. Eng., Xi''an Jiaotong Univ. ; Chongzhao Han ; Ming Lei

A novel knowledge discovery method to multiple classifier fusion is proposed. In the new method, all base classifiers are viewed as predictors relating to domain knowledge, and they may be allowed to operate in different feature spaces. Then the beliefs assigned to each base classifier are generated automatically from the established decision tables (DTs). For this purpose, two types of belief structures on DT are investigated based on generalized rough set model and Dempster-Shafer theory (DST). Correspondingly, two fusion approaches are designed based on the belief structures and the heuristic fusion function. Compared with plurality voting, the vegetation classification experiment on hyperspectral remote sensing images shows that the performance of the classification can be improved further by using the proposed method

Published in:

Information Fusion, 2006 9th International Conference on

Date of Conference:

10-13 July 2006