Diversity Measures for Building Multiple Classifier Systems Using Genetic Algorithms

Leidys Cabrera Hernández, Alejandro Morales Hernández, Gladys María Casas Cardoso


In this paper we present the different diversity measures that exist in the literature to decide if a set of classifiers is diverse, aspect that is very important in the creation of multi-classifier systems. The modeling forbuilding multi-classifier systems using meta-heuristic of Genetic Algorithm to ensure the best possible accuracy and greater diversity among the classifiers is presented. Various forms of combination for diversity measures are also enunciated. Finally, we discuss two experiments in which the individual behaviors of diversity measures and their combinations are analyzed.


Diversity measures; multi-classifier; classifiers; genetic algorithms.

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