Multiobjective Supervised Learning

When dealing with computationally expensive simulation codes or process measurement data, surrogate modeling methods are firmly established as facilitators for design space exploration, sensitivity analysis, visualization, prototyping and optimization. Typically the model parameter (=hy-perparameter) optimization problem as part of global surrogate modeling is formulated in a single objective way. Models are generated according to a single objective (accuracy). However, this requires an engineer to determine a single accuracy target and measure upfront, which is hard to do if the behavior of the response is unknown. Likewise, the different outputs of a multi-output system are typically modeled separately by independent models. Again, a multiobjective approach would benefit the domain expert by giving information about output correlation and enabling automatic model type selection for each output dynamically. With this paper the authors attempt to increase awareness of the subtleties involved and discuss a number of solutions and applications. In particular we present a multiobjective framework for automatic global surrogate model generation to help tackle both problems and that is applicable in both the static and sequential design (adaptive sampling) case.

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Multiobjective global surrogate modeling, dealing with the 5-percent problem

Gorissen

Couckuyt

Laermans

et al. 2009

Engineering with Computers

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The Multiclass ROC Front method for cost-sensitive classification

Bernard¹,

Chatelain²,

Adam³

et al. 2016

Pattern Recognition

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International audienceThis paper addresses the problem of learning a multiclass classification system that can suits to any environment. By that we mean that particular (imbalanced) misclassification costs are taken into account by the classifier for predictions. However, these costs are not well known during the learning phase in most cases, or may evolve afterwards. There is a need in that case to learn a classifier that can potentially suits to any of these costs in prediction phase. The learning method proposed in this work, named the Multiclass ROC Front (MROCF) method, respond to this issue by exploiting ROC-based tools through a multiobjective optimization process. While this type of ROC-based multiobjective optimization approach has been successfully used for two-class problems, it has never been proposed in real-world multiclass classification problems. Experiments led on several real-world datasets show that the MROCF method offers a major improvement over a cost-insensitive classifier and is competitive with the state-of-the-art cost-sensitive optimization method on all but one of the 20 datasets

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Multiobjective Supervised Learning

Cited by 2 publications

References 28 publications

Multiobjective global surrogate modeling, dealing with the 5-percent problem

Multiobjective global surrogate modeling, dealing with the 5-percent problem

The Multiclass ROC Front method for cost-sensitive classification

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