Entrepôts, Représentation et Ingénierie des Connaissances
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- Using set functions for multiple classifiers combination

Author(s): Rolland A., Rico F.

Conference: DA2PL'12 (, FR, 2012-11-15)
Proceedings: DA2PL 2012, actes, vol. p.57-62 (2012)


In machine learning, the multiple classifiers aggregation problems consist in using multiple classifiers to enhance the quality of a single classifier. Simple classifiers as mean or majority rules are already used, but the aggregation methods used in voting theory or multi-criteria decision making should increase the quality of the obtained results. Meanwhile, these methods should lead to better interpretable results for a human decision-maker. We present here the results of a first experiment based on the use of Choquet integral, decisive sets and rough sets based methods on four different datasets.