Publications by authors named "Sebastien Destercke"

While risk-based contaminated land management is an essential component of sustainable remediation, uncertainty is an unavoidable aspect of risk assessment, since most of the parameters that influence risk are typically affected by uncertainty. Uncertainty may be of different origins; i.e.

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Agrifood chain processes are based on a multitude of knowledge, know-how and experiences forged over time. This collective expertise must be shared to improve food quality. Here we test the hypothesis that it is possible to design and implement a comprehensive methodology to create a knowledge base integrating collective expertise, while also using it to recommend technical actions required to improve food quality.

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Single nucleotide variants (SNVs) occurring in a protein coding gene may disrupt its function in multiple ways. Predicting this disruption has been recognized as an important problem in bioinformatics research. Many tools, hereafter p-tools, have been designed to perform these predictions and many of them are now of common use in scientific research, even in clinical applications.

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Indeterminate classifiers are cautious models able to predict more than one class in case of high uncertainty. A problem that arises when using such classifiers is how to evaluate their performances. This problem has already been considered in the case where all prediction errors have equivalent costs (that we will refer as the "0/1 costs" or accuracy setting).

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Combining pieces of information provided by several sources without or with little prior knowledge about the behavior of the sources is an old yet still important and rather open problem in the belief function theory. In this paper, we propose an approach to select the behavior of sources based on a very general and expressive fusion scheme, that has the important advantage of making clear the assumptions made about the sources. The selection process itself relies on two cornerstones that are the notions of specificity and consistency of a knowledge representation, and that we adapt to the considered fusion scheme.

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Recently, the problem of measuring the conflict between two bodies of evidence represented by belief functions has known a regain of interest. In most works related to this issue, Dempster's rule plays a central role. In this paper, we propose to study the notion of conflict from a different perspective.

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Synopsis of recent research by authors named "Sebastien Destercke"

  • - Sebastien Destercke's research focuses on the application of uncertainty management in various domains such as environmental science, agrifood chains, bioinformatics, and decision-making processes, emphasizing the importance of integrating collective expertise and robust methodologies to address uncertainty effectively
  • - His recent studies propose innovative frameworks, including risk-based approaches for soil quality objectives and methodologies for knowledge integration in agrifood production, showcasing significant implications for improving sustainability and food quality
  • - Destercke also explores theoretical aspects of uncertainty in decision-making and information fusion, contributing new perspectives on classifying predictive accuracy and the conflict between belief functions in knowledge representation