Abstract
In a previous paper the use of GAs as an editing technique for the k-nearest neighbor ( k- NN) classification technique has been suggested. Here we are looking at different fitness functions. An experimental study with the IRIS data set and with a medical data set has been carried out. Best results (smallest subsets with highest test classification accuracy) have been obtained by including in the fitness function a penalizing term accounting for the cardinality of the reference set.
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