What about interpolation?

Abstract

Learning Classifier Systems (LCS) have been strongly investigated in the context of regression tasks and great successes have been achieved by applying the function approximating Extended Classifier System (XCSF) endowed with sophisticated prediction models. In this paper, a novel approach to model a classifier's payoff prediction is proposed. Radial Basis Function (RBF) interpolation is utilized as a new means to capture the underlying function surface complexity. We pose the hypothesis that by the use of a more flexible RBF-based classifier prediction, that alleviates the a priori bias injected via choosing the degree of a polynomial approximation, the classifiers can evolve toward a higher generality by maintaining at least a competitive level of performance compared to the current and probably mostly used state of the art approach - polynomial approximation in combination with the Recursive Least Squares (RLS) technique for incremental coefficient optimization. The presented experimental results underpin our assumptions by revealing that the RBF-based classifier prediction outperforms the n-th order polynomial approximation on several test functions of varying complexity. Additionally, results of experiments with various degrees of noise will be reported to touch upon the proposed approach's applicability in real world situations.