A comparison of relative accuracy and raw accuracy in XCS

In XCS classifier fitness is measured as the relative accuracy of classifier prediction. A classifier is fit if its prediction of the expected payoff is more accurate than that provided by the other classifiers that appear in the same environmental niches. We introduce a modification of Wilson´s original definition in which classifier fitness is measured as the absolute (raw) accuracy of classifier prediction. A classifier is fit if the error affecting its prediction is smaller than a given threshold. Then we compare Wilson´s relative accuracy and raw accuracy on a number of problems both in terms of learning performance and in terms of generalization capabilities.