Evidential uncertainty sampling for active learning
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916259488071680 |
|---|---|
| author | Hoarau, Arthur Lemaire, Vincent Martin, Arnaud Dubois, Jean-Christophe Gall, Yolande Le |
| author_facet | Hoarau, Arthur Lemaire, Vincent Martin, Arnaud Dubois, Jean-Christophe Gall, Yolande Le |
| contents | Recent studies in active learning, particularly in uncertainty sampling, have focused on the decomposition of model uncertainty into reducible and irreducible uncertainties. In this paper, the aim is to simplify the computational process while eliminating the dependence on observations. Crucially, the inherent uncertainty in the labels is considered, the uncertainty of the oracles. Two strategies are proposed, sampling by Klir uncertainty, which tackles the exploration-exploitation dilemma, and sampling by evidential epistemic uncertainty, which extends the concept of reducible uncertainty within the evidential framework, both using the theory of belief functions. Experimental results in active learning demonstrate that our proposed method can outperform uncertainty sampling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_12494 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Evidential uncertainty sampling for active learning Hoarau, Arthur Lemaire, Vincent Martin, Arnaud Dubois, Jean-Christophe Gall, Yolande Le Machine Learning Recent studies in active learning, particularly in uncertainty sampling, have focused on the decomposition of model uncertainty into reducible and irreducible uncertainties. In this paper, the aim is to simplify the computational process while eliminating the dependence on observations. Crucially, the inherent uncertainty in the labels is considered, the uncertainty of the oracles. Two strategies are proposed, sampling by Klir uncertainty, which tackles the exploration-exploitation dilemma, and sampling by evidential epistemic uncertainty, which extends the concept of reducible uncertainty within the evidential framework, both using the theory of belief functions. Experimental results in active learning demonstrate that our proposed method can outperform uncertainty sampling. |
| title | Evidential uncertainty sampling for active learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2309.12494 |