Communication Dans Un Congrès Année : 2024

Robust Discrete Bayesian Classifier Under Covariate and Label Noise

Résumé

In this paper, we focus on the Discrete Bayesian Classifier (DBC), which discretizes the input space into regions where class probabilities are estimated. We investigate fuzzy partitioning as an alternative to the hard partitioning classically used to discretize the space. We show that our approach not only boosts the DBC’s performance and resilience to noise, but also mitigates the loss of information due to discretization. The benefits of soft partitioning are demonstrated experimentally on several synthetic and real datasets.
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Dates et versions

hal-04885949 , version 1 (14-01-2025)

Identifiants

Citer

Wenlong Chen, Cyprien Gilet, Benjamin Quost, Sébastien Destercke. Robust Discrete Bayesian Classifier Under Covariate and Label Noise. 16th International Conference on Scalable Uncertainty Management (SUM 2024), Nov 2024, Palermo (Italy), Italy. pp.100-114, ⟨10.1007/978-3-031-76235-2_8⟩. ⟨hal-04885949⟩
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