Be real! XCS with continuous-valued inputs

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

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Abstract

XCS is widely accepted as one of the most reliable Michigan-style learning classifier system (LCS) for data mining. In order to handle real-valued inputs effectively, the traditional ternary representation has been replaced by the interval-based representation and the modified XCS has shown to work well. Existing interval-based representations still suffer from a few drawbacks which this paper address. In this paper, we propose an alternative approach called the Min-Percentage representation which produces comparable results to other methods in the literature with the extra advantage of overcoming the drawbacks in these methods.