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Using Raw Accuracy to Estimate Classifier Fitness in XCS
Pier Luca Lanzi
Artificial Intelligence and Robotics Laboratory
Dipartimento di Elettronica e Informazione
Politecnico di Milano
pierluca.lanzi@polimi.it
Abstract.
In XCS classifier fitness is based on the relative accuracy
of the classifier prediction [3]. A classifier is more fit if its
prediction of the expected payoff is more accurate than the
prediction given by the other classifiers that are applied in the
same situations. The use of relative accuracy has two major
implications. First, because the evaluation of fitness is based on
the relevance that classifiers have in some situations,
classifiers that are the only ones applying in a certain situation
have a high fitness, even if they are inaccurate. As a
consequence, inaccurate classifiers might be able to reproduce so
to cause reduced performance; as already noted by Wilson (personal
communication reported in [1]). In addition, because the
computation of classifier fitness is based both (i) on the
classifier accuracy and (ii) on the classifier relevance in
situations in which it applies, in XCS, classifier fitness does
not provide information about the problem solution, but rather an
indication of the classifier relevance in the encountered
situations. Accordingly, it is not generally possible to tell
whether a classifier with a high fitness is accurate or not, just
looking at the fitness. To have this kind of information, we need
the prediction error 
which provides an indication of
the raw classifier accuracy.
LNCS 2724, p. 1922 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2003