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Distributed learning classifier systems
Author(s)
Dam, Hai H.
Rojanavasu, Pornthep
Abbass, Hussein A.
Lokan, Chris
Date Issued
July 16, 2008
Type
Article
Abstract
Genetics-based machine learning methods - also called learning classifier systems (LCSs) - are evolutionary computation based data mining techniques. The advantages of these techniques are: to provide rule-based models that represent human-readable patterns; to learn incrementally, capable of adapting quickly to any changes in dynamic environments; and some of them have linear 0(n) learning complexity in the size of data set. However, not too much effort has yet been put into investigating LCSs in distributed environments. This chapter will scrutinize several issues of LCSs in distributed environments such as knowledge passing in the system, knowledge combination methods at the central location, and the effect on the system's learning accuracy of having different numbers of distributed sites. © 2008 Springer-Verlag Berlin Heidelberg.
Citation
Studies in Computational Intelligence, 125, 69-91, 2008