A Robust Credal Assignment Solution Based on the Generalized Bayes’ Theorem
| Autor: | Samir Hachour, François Delmotte, David Mercier |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Mathematical optimization Uncertain data Data assignment 02 engineering and technology Synthetic data k-nearest neighbors algorithm Bayes' theorem 020901 industrial engineering & automation Artificial Intelligence Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Belief function theory Best matching Software Generalized assignment problem Information Systems Mathematics |
| Zdroj: | International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. 25:947-971 |
| ISSN: | 1793-6411 0218-4885 |
| DOI: | 10.1142/s0218488517500416 |
| Popis: | This paper proposes a new assignment solution based on the Generalized Bayes’ Theorem (GBT) which aims to establish the best matching between two sets of uncertain data. In order to estimate the effectiveness of the proposition, it is compared to the best credal assignment solutions and the well known Global Nearest Neighbor (GNN) algorithm, through synthetic data and a literature example of multi-target tracking scenarios. Given the same input data, the proposed solution gives better assignment results, especially when sensor imprecision increases. However, the proposed solution stills actually computationally more complex than the GNN and the solution proposed by Denoeux et al. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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