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S\'ebastien Destercke

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2 papers

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ISIPTA Conference 2025 Conference Paper

Credal discrete classifier

Wenlong Chen
Cyprien Gilet
Benjamin Quost
S\'ebastien Destercke

This paper presents a novel extension of the discrete Bayesian classifier (DBC) into a set-valued classification framework grounded in imprecise probability theory. The standard DBC framework, which relies on partitioning the input space into profiles and estimating class-conditional probabilities, may not be very robust to distribution changes or imperfections in observed data. In the hope to mitigate such issues, we introduce the Credal Discrete Classifier (CDC), an imprecise-probabilistic extension of the traditional Bayesian approach. By representing uncertainties in the estimated probabilities through belief functions, CDC offers interval-valued risks and set-valued decisions, thereby enhancing robustness. Experimental results on several benchmark datasets demonstrate that CDC effectively balances accuracy and determinacy by allowing for set-valued predictions in uncertain contexts, often outperforming or matching traditional precise classifiers.

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ISIPTA Conference 2025 Conference Paper

On the value of varied evidence for imprecise probabilities

J\"urgen Landes
S\'ebastien Destercke

It has long been considered a truism that we can learn more from a variety of sources than from highly correlated sources. This truism is captured by the Variety of Evidence Thesis. To the surprise of many, this thesis turned out to fail in a number of Bayesian settings. In other words, replication can trump variation. Translating the thesis into IP we obtain two distinct, a priori plausible formulations in terms of ‘increased confirmation’ and ‘uncertainty reduction’, respectively. We investigate both formulations, which both fail for different parameters and different reasons, that cannot be predicted prior to formal analysis. The emergence of two distinct formulations distinguishing confirmation increase from uncertainty reduction, which are conflated in the Bayesian picture, highlights fundamental differences between IP and Bayesian reasoning.

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