Unsupervised rough set classification using GAs

Authors
Citation
P. Lingras, Unsupervised rough set classification using GAs, J INTELL IN, 16(3), 2001, pp. 215-228
Citations number
25
Categorie Soggetti
Information Tecnology & Communication Systems
Journal title
JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
ISSN journal
09259902 → ACNP
Volume
16
Issue
3
Year of publication
2001
Pages
215 - 228
Database
ISI
SICI code
0925-9902(200108)16:3<215:URSCUG>2.0.ZU;2-C
Abstract
The rough set is a useful notion for the classification of objects when the available information is not adequate to represent classes using precise s ets. Rough sets have been successfully used in information systems for lear ning rules from an expert. This paper describes how genetic algorithms can be used to develop rough sets. The proposed rough set theoretic genetic enc oding will be especially useful in unsupervised learning. A rough set genom e consists of upper and lower bounds for sets in a partition. The partition may be as simple as the conventional expert class and its complement or a more general classification scheme. The paper provides a complete descripti on of design and implementation of rough set genomes. The proposed design a nd implementation is used to provide an unsupervised rough set classificati on of highway sections.