DocumentCode
2244917
Title
Fuzzy rules from ant-inspired computation
Author
Galea, Michelle ; Shen, Qiang
Author_Institution
Sch. of Informatics, Edinburgh Univ., UK
Volume
3
fYear
2004
fDate
25-29 July 2004
Firstpage
1691
Abstract
A new approach to fuzzy rule induction from historical data is presented. The implemented system - FRANTIC - is a tested on a simple classification problem against a fuzzy tree induction algorithm, a genetic algorithm, and a numerical method for inducing fuzzy rules based on fuzzy subsethood values. The results obtained by FRANTIC indicate comparable or better classification accuracy, superior comprehensibility, and potentially more flexibility when applied to larger data sets. The impact of the knowledge representation used when generating fuzzy rules is also highlighted.
Keywords
fuzzy set theory; genetic algorithms; knowledge representation; tree data structures; ant-inspired computation; classification accuracy; fuzzy rule; fuzzy tree induction algorithm; genetic algorithm; knowledge representation; optimisation; Classification tree analysis; Clustering algorithms; Fuzzy systems; Genetic algorithms; Induction generators; Informatics; Insects; Knowledge representation; Partitioning algorithms; System testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems, 2004. Proceedings. 2004 IEEE International Conference on
ISSN
1098-7584
Print_ISBN
0-7803-8353-2
Type
conf
DOI
10.1109/FUZZY.2004.1375435
Filename
1375435
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