Name
Abstract | ||
|---|---|---|
Uncertainty is at the heart of decision-making processes in most real-world applications. Uncertainty can be broadly categorized into two types: aleatory and epistemic. Aleatory uncertainty describes the variability in the physical system where sensors provide information (hard) of a probabilistic type. Epistemic uncertainty appears when the information is incomplete or vague such as judgments or human expert appreciations in linguistic form. Linguistic information (soft) typically introduces a possibilistic type of uncertainty. This paper is concerned with the problem of classification where the available information, concerning the observed features, may be of a probabilistic nature for some features, and of a possibilistic nature for some others. In this configuration, most encountered studies transform one of the two information types into the other form, and then apply either classical Bayesian-based or possibilistic-based decision-making criteria. In this paper, a new hybrid decision-making scheme is proposed for classification when hard and soft information sources are present. A new Possibilistic Maximum Likelihood (PML) criterion is introduced to improve classification rates compared to a classical approach using only information from hard sources. The proposed PML allows to jointly exploit both probabilistic and possibilistic sources within the same probabilistic decision-making framework, without imposing to convert the possibilistic sources into probabilistic ones, and vice versa. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3390/e23010067 | ENTROPY |
Keywords | DocType | Volume |
possibility theory, possibilistic decision rule, possibilistic maximum likelihood, pattern classification, uncertainty, Bayesian decision, maximum a posteriori, image processing | Journal | 23 |
Issue | ISSN | Citations |
1 | 1099-4300 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Basel Solaiman | 1 | 127 | 35.05 |
| Didier Gueriot | 2 | 30 | 6.20 |
| Shaban Almouahed | 3 | 21 | 6.73 |
| Bassem Alsahwa | 4 | 9 | 2.62 |
| Éloi Bossé | 5 | 386 | 26.19 |