Sampling aspects of rough set theory

Sampling aspects of rough set theory

0.00 Avg rating0 Votes
Article ID: iaor20071511
Country: Germany
Volume: 1
Issue: 2
Start Page Number: 151
End Page Number: 178
Publication Date: Apr 2004
Journal: Computational Management Science
Authors:
Abstract:

Rough Set Theory (RST) originated as an approach to approximating a given set, but has found its main applications in the statistical domain of classification problems. It generates classification rules, and can be seen in general terms as a technique for rule induction. Expositions of RST often stress that it is robust in requiring no (explicit) assumptions of a statistical nature. The argument here, however, is that this apparent strength is also a weakness which prevents establishment of general statistical properties and comparison with other methods. A sampling theory is developed for the first time, using both the original RST model and its probabilistic extension, Variable Precision Rough Sets. This is applied in the context of examples, one of which involves Fisher's Iris data.

Reviews

Required fields are marked *. Your email address will not be published.