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Post-processing of classifier outputs in multiple classifier systems

Altincay, H
Demirekler, Mübeccel
Incomparability in classifier outputs due to the variability in their scales is a major problem in the combination of different classification systems. In order to compensate this, output normalization is generally performed where the main aim is to transform the outputs onto the same scale. In this paper, it is proposed that in selecting the transformation function, the scale similarity goal should be accomplished with two more requirements. The first one is the separability of the pattern classes in the transformed output space and the second is the compatibility of the outputs with the combination rule. A method of transformation that provides improved satisfaction of the additional requirements is proposed which is shown to improve the classification performance of both linear and Bayesian combination systems based on the use of confusion matrix based a posteriori probabilities....