Dealing with Atypical Instances in Evidential Decision-Making
| Autor: | Marie-Hélène Masson, Sébastien Destercke, Benjamin Quost |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
education.field_of_study
Training set Computer science business.industry media_common.quotation_subject Population 02 engineering and technology Machine learning computer.software_genre Novelty detection Scarcity 020204 information systems 0202 electrical engineering electronic engineering information engineering Natural (music) 020201 artificial intelligence & image processing Artificial intelligence Uncertainty quantification education Function (engineering) business computer Mixing (physics) media_common |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030584481 SUM |
| DOI: | 10.1007/978-3-030-58449-8_15 |
| Popis: | When classifying an example on the basis of an observed population of (training) samples, at least three kinds of situations can arise where picking a single class may be difficult: high aleatory uncertainty due to the natural mixing of classes, high epistemic uncertainty due to the scarcity of training data, and non-conformity or atypicality of the example with respect to observations made so far. While the two first kinds of situations have been explored extensively, the last one still calls for a principled analysis. This paper is a first proposal to address this issue within the theory of belief function. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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