An efficient classifier design integrating Rough Set and Dempster-Shafer Theory Online publication date: Thu, 17-Feb-2011
by Asit Kumar Das, Jaya Sil
International Journal of Artificial Intelligence and Soft Computing (IJAISC), Vol. 2, No. 3, 2010
Abstract: An integrated approach of knowledge discovery has been proposed in the paper using Rough Set Theory (RST) and Dempster-Shafer's (D-S) theory where high dimensional data is reduced in two folds. Firstly, unimportant attributes are eliminated using RST generating minimal subset of attributes, called reducts. Considering each core attribute as root of a decision tree, classification rules are built and grouped based on some similarity measure. Representative of each group constitute the new rule set and thus rules has been reduced while important information are retained. D-S theory ensembles the rules from which a classifier with highest accuracy has been selected.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Artificial Intelligence and Soft Computing (IJAISC):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com