A Generalized Framework for Multi-Criteria Classifiers with Automated Learning: application on FLIR Ship Imagery

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1 December 2009
Khaled Jabeur, Adel Guitouni

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This paper reviews Multi-Criteria Classifiers (MCCs) or commonly multi-criteria classification methods. These methods have many advantages including flexibility, the integration of human judgments and prevention of black box syndrome. However, these advantages come with a price: large number of parameters to be setup. In particular, this paper focuses on Nominal Concordance/ Discordance-based MCCs (NCD-MCCs). A generalized framework is proposed to synthesize the underlying computation algorithm for each MCC. In order to address MCCs disadvantages, an Automated Learning Method (ALM) based on Real-Coded Genetic Algorithm (RCGA) is proposed to infer these parameters. The empirical results of some MCCs are compared with those obtained by other classifiers (e.g. Bayes and Dempster-Shafer classifiers). A military dataset of 2545 Forward Looking Infra-Red (FLIR) images representing eight different classes of ships is therefore used to test the performance of these classifiers. In this paper, we argue the benefits of cross-fertilization of MCCs and information fusion algorithms.