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Handling Mixture Optimisation Problem Using Cautious Predictions and Belief Functions

Abstract : Predictions from classification models are most often used as final decisions. Yet, there are situations where the prediction serves as an input for another constrained decision problem. In this paper, we consider such an issue where the classifier provides imprecise and/or uncertain predictions that need to be managed within the decision problem. More precisely, we consider the optimisation of a mix of material pieces of different types in different containers. Information about those pieces is modelled by a mass function provided by a cautious classifier. Our proposal concerns the statement of the optimisation problem within the framework of belief function. Finally, we give an illustration of this problem in the case of plastic sorting for recycling purposes.
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https://hal.mines-ales.fr/hal-02872104
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Submitted on : Thursday, June 25, 2020 - 9:21:44 AM
Last modification on : Wednesday, October 21, 2020 - 2:25:57 PM
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Lucie Jacquin, Abdelhak Imoussaten, Sébastien Destercke. Handling Mixture Optimisation Problem Using Cautious Predictions and Belief Functions. 18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2020), Jun 2020, Lisboa, Portugal. pp.394-407, ⟨10.1007/978-3-030-50143-3_30⟩. ⟨hal-02872104⟩

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