Generating Sets of Classifiers for the Evaluation of Multi-expert Systems

This paper addresses the problem of multi-classifier system evaluation by artificially generated classifiers. For the purpose, a new technique is presented for the generation of sets of artificial abstract-level classifiers with different characteristics at the individual-level (i.e. recognition performance) and at the collective-level (i.e. degree of similarity). The technique has been used to generate sets of classifiers simulating different working conditions in which the performance of combination methods can be estimated. The experimental tests demonstrate the effectiveness of the approach in generating simulated data useful to investigate the performance of combination methods for abstract-level classifiers.