improving generalization in the xcsf classifier system using linear least-squares
XCSF is an extension of XCS in which classifier prediction is computed as a linear combination of classifier inputs and a weight vector associated to each classifier. XCSF can adjust the weight vector of classifiers to evolve accurate piecewise linear approximations of functions. The Widrow-Hoff rule, used to update the weight vectors, prevents (when some conditions hold) XCSF from exploiting the expected piece-wise linear approximation. In this paper we replace the Widrow-Hoff rule with linear least-squares and we show that with this improvement XCSF can fully exploit its generalization capabilities.
generalization xcsf least-squares function approximation learning classifier systems