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.

A Comparison of Relative Accuracy and Raw Accuracy in XCS

LANZI, PIER LUCA
2003-01-01

Abstract

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.
Proceedings of the 2003 Congress on Evolutionary Computation (CEC 2003)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/569577
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