Unsupervised Rough Set Classification Using GAs

The rough set is a useful notion for the classification of objects when the available information is not adequate to represent classes using precise sets. Rough sets have been successfully used in information systems for learning rules from an expert. This paper describes how genetic algorithms can...

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Bibliographic Details
Published in:Journal of intelligent information systems 2001-08, Vol.16 (3), p.215
Main Author: Lingras, Pawan
Format: Article
Language:English
Subjects:
Algorithms
Approximation
Classification
Classification schemes
Evolution
Generations
Genetic algorithms
Genomes
Information systems
Learning
Mutation
Recreation
Set theory
Studies
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Summary:The rough set is a useful notion for the classification of objects when the available information is not adequate to represent classes using precise sets. Rough sets have been successfully used in information systems for learning rules from an expert. This paper describes how genetic algorithms can be used to develop rough sets. The proposed rough set theoretic genetic encoding will be especially useful in unsupervised learning. A rough set genome 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 description of design and implementation of rough set genomes. The proposed design and implementation is used to provide an unsupervised rough set classification of highway sections. [PUBLICATION ABSTRACT]
ISSN:0925-9902
1573-7675
DOI:10.1023/A:1011219918340