Journal
THEORETICAL COMPUTER SCIENCE
Volume 412, Issue 42, Pages 5871-5884Publisher
ELSEVIER
DOI: 10.1016/j.tcs.2011.05.040
Keywords
Fuzzy-rough sets; Classification; Prediction; Nearest neighbours
Categories
Funding
- Research Foundation-Flanders
Ask authors/readers for more resources
Nearest neighbour (NN) approaches are inspired by the way humans make decisions, comparing a test object to previously encountered samples. In this paper, we propose an NN algorithm that uses the lower and upper approximations from fuzzy-rough set theory in order to classify test objects, or predict their decision value. It is shown experimentally that our method outperforms other NN approaches (classical, fuzzy and fuzzy-rough ones) and that it is competitive with leading classification and prediction methods. Moreover, we show that the robustness of our methods against noise can be enhanced effectively by invoking the approximations of the Vaguely Quantified Rough Set (VQRS) model, which emulates the linguistic quantifiers some and most from natural language. Crown Copyright (C) 2011 Published by Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available