Title :
Accuracy boosting induction of fuzzy rules with Artificial Immune Systems
Author :
Adam Kalina;Edward Mezyk;Olgierd Unold
Author_Institution :
Value Based Advisors Sp. z o.o., ul. Po?abian 35, 52-339 Wroc?aw, Poland
Abstract :
The paper introduces accuracy boosting extension to a novel induction of fuzzy rules from raw data using artificial immune system methods. Accuracy boosting relies on fuzzy partition learning. The modified algorithm was experimentally proved to be more accurate for all learning sets containing non-crisp attributes.
Keywords :
"Boosting","Fuzzy systems","Artificial immune systems","Fuzzy sets","Data mining","Partitioning algorithms","Genetic algorithms","Computer science","Information technology","Control engineering computing"
Conference_Titel :
Computer Science and Information Technology, 2008. IMCSIT 2008. International Multiconference on
Print_ISBN :
978-83-60810-14-9
DOI :
10.1109/IMCSIT.2008.4747233
