Adaptive Score Normalization for Output Integration in Multiclassifier Systems

This letter introduces a new score normalization technique - based on Dynamic Time Warping (DTW) - for output integration in multiclassifier systems. More precisely, DTW is used to match the score cumulative distribution of each individual classifier against a standard cumulative distribution. The warping function allows optimal alignment of the scores provided by the individual classifiers with the scores on the standard cumulative distribution. Furthermore, in order to adapt the normalization process to the behavior of the individual classifiers and to the decision fusion rule, a new class of fuzzy cumulative distributions is introduced and a genetic approach is used to select the optimal distribution to be used as standard cumulative distribution for score normalization. The experimental tests report better results for the fuzzy normalization technique than for those obtained with other approaches present in the literature.