The Impact of the Prototype Selection on a Multicriteria Decision Aid Classification Algorithm

This paper presents an experimental analysis conducted over a specific Multicriteria Decision Aid (MCDA) classification technique proposed earlier by Goletsis et al. Different from other studies on MCDA classifiers, which put more emphasis on the calibration of some control parameters related to the expert's preference modeling process, this work investigates the impact that the prototype selection task exerts on the classification performance exhibited by the MCDA model under analysis. We understand that this sort of empirical assessment is interesting as it reveals how robust/sensitive a MCDA classifier could be to the choice of the alternatives (samples) that serve as class representatives for the problem in consideration. In fact, the experiments we have realized so far, involving different datasets from the UCI repository, reveal that the proper choice of the prototypes can be a rather determinant issue to leverage the classifier's performance.