Cautious classification based on belief functions theory and imprecise relabelling

Imoussaten, Abdelhak; Jacquin, Lucie

doi:10.1016/j.ijar.2021.11.009

Cited by 7 publications

(1 citation statement)

References 39 publications

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“…The approach in [8] uses the interval dominance approach where the intervals are represented by the values of belief and plausibility functions obtained of each class. In [5] [4], the evidential classifier based on imprecise relabelling (eclair ) uses a generalisation of the gain function proposed in [3] to the case of belief functions framework. An imprecise classifier proposes the appropriate subset of candidate classes that can be assigned to the sample in the presence of imperfect information.…”

Section: Introductionmentioning

confidence: 99%

Choosing the Decision Hyper-parameter for Some Cautious Classifiers

Imoussaten¹

2022

Information Processing and Management of Uncertainty in Knowledge-Based Systems

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Section: Introductionmentioning

confidence: 99%