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Multi-Objective Evolutionary Optimization for Generating Ensembles of Classiﬁers in the ROC Space

Julien-Charles Lévesque, Audrey Durand, Christian Gagné and Robert Sabourin
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Abstract - In this paper, we propose a novel approach for the multi-objective optimization of classifier ensembles in the ROC space. We first evolve a pool of simple classifiers with NSGA-II using values of the ROC curves as the optimization objectives. These simple classifiers are then combined at the decision level using the Iterative Boolean Combination method (IBC). This method produces multiple ensembles of classifiers optimized for various operating conditions. We perform a rigorous series of experiments to demonstrate the properties and behaviour of this approach. This allows us to propose interesting venues for future research on optimizing ensembles of classifiers using multi-objective evolutionary algorithms.

Bibtex:

@inproceedings{Lévesque940,
author    = { Julien-Charles Lévesque and Audrey Durand and Christian Gagné and Robert Sabourin },
title     = { Multi-Objective Evolutionary Optimization for Generating Ensembles of Classiﬁers in the ROC Space },
booktitle = { Proc. of the Genetic and Evolutionary Computation Conference (GECCO 2012) },
year      = { 2012 },
month     = { July 07-11 },
location  = { Philadelphia (PA), USA },
web       = { http://dx.doi.org/10.1145/2330163.2330285 }
}

Dernière modification: 2012/06/12 par cgagne

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