A multi-model selection framework for unknown and/or evolutive misclassification cost problems |
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Authors: | Clément Chatelain [Author Vitae]Author Vitae] Yves Lecourtier [Author Vitae] [Author Vitae] Thierry Paquet [Author Vitae] |
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Affiliation: | Université de Rouen, LITIS EA 4108, BP12, 76801 Saint Etienne du Rouvray, France |
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Abstract: | In this paper, we tackle the problem of model selection when misclassification costs are unknown and/or may evolve. Unlike traditional approaches based on a scalar optimization, we propose a generic multi-model selection framework based on a multi-objective approach. The idea is to automatically train a pool of classifiers instead of one single classifier, each classifier in the pool optimizing a particular trade-off between the objectives. Within the context of two-class classification problems, we introduce the “ROC front concept” as an alternative to the ROC curve representation. This strategy is applied to the multi-model selection of SVM classifiers using an evolutionary multi-objective optimization algorithm. The comparison with a traditional scalar optimization technique based on an AUC criterion shows promising results on UCI datasets as well as on a real-world classification problem. |
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Keywords: | ROC front Multi-model selection Multi-objective optimization ROC curve Handwritten digit/outlier discrimination |
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