DXCS: an XCS system for distributed data mining

Authors:
Hai H. Dam;Hussein A. Abbass;Chris Lokan
Affiliations:
School of ITEE, UNSW@ADFA, Canberra, Australia;School of ITEE, UNSW@ADFA, Canberra, Australia;School of ITEE, UNSW@ADFA, Canberra, Australia
Venue:
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Year:
2005

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Abstract

XCS is a flexible system for data mining due to its ability to deal with environmental changes, learn online with little prior knowledge and evolve accurate and maximally general classifiers. In this paper, we propose DXCS which is an XCS-based distributed data mining system. A MDL metric is proposed to quantify and analyze network load, and study the balance between network load and classifier accuracy in the presence of noise. The DXCS system shows promising results.