Machine learning with a reject option: a survey
Abstrak
Machine learning models always make a prediction, even when it is likely to be inaccurate. This behavior should be avoided in many decision support applications, where mistakes can have severe consequences. Albeit already studied in 1970, machine learning with rejection recently gained interest. This machine learning subfield enables machine learning models to abstain from making a prediction when likely to make a mistake. This survey aims to provide an overview on machine learning with rejection. We introduce the conditions leading to two types of rejection, ambiguity and novelty rejection, which we carefully formalize. Moreover, we review and categorize strategies to evaluate a model’s predictive and rejective quality. Additionally, we define the existing architectures for models with rejection and describe the standard techniques for learning such models. Finally, we provide examples of relevant application domains and show how machine learning with rejection relates to other machine learning research areas.
Topik & Kata Kunci
Penulis (5)
Kilian Hendrickx
Lorenzo Perini
Dries Van der Plas
Wannes Meert
Jesse Davis
Akses Cepat
- Tahun Terbit
- 2021
- Bahasa
- en
- Total Sitasi
- 174×
- Sumber Database
- Semantic Scholar
- DOI
- 10.1007/s10994-024-06534-x
- Akses
- Open Access ✓