Amb motiu del tancament d'estiu, la validació de documents es reprendrà a partir del 28 d'agost de 2026. Disculpeu les molèsties.
Con motivo del cierre de verano, la validación de documentos se reanudará a partir del 28 de agosto de 2026. Disculpad las molestias
Due to the summer closure, document validation will resume starting August 28, 2026. We apologize for any inconvenience.

Files

700793.pdf (4.48 MB)

Document type

Article

Version

Published version

Publication date

2020-05-21

Publication license

cc-by (c) Mena, José et al., 2020
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/168537

Uncertainty-based Rejection Wrappers for Black-box Classifiers

Journal Title

Authors

Director/Tutor

Journal ISSN

Volume Title

Related resource

https://doi.org/10.1109/ACCESS.2020.2996495

Abstract

Machine Learning as a Service platform is a very sensible choice for practitioners that wantto incorporate machine learning to their products while reducing times and costs. However, to benefit theiradvantages, a method for assessing their performance when applied to a target application is needed. In thiswork, we present a robust uncertainty-based method for evaluating the performance of both probabilistic andcategorical classification black-box models, in particular APIs, that enriches the predictions obtained withan uncertainty score. This uncertainty score enables the detection of inputs with very confident but erroneouspredictions while protecting against out of distribution data points when deploying the model in a productivesetting. We validate the proposal in different natural language processing and computer vision scenarios.Moreover, taking advantage of the computed uncertainty score, we show that one can significantly increasethe robustness and performance of the resulting classification system by rejecting uncertain predictions

Subject

Aprenentatge automàtic, Sistemes classificadors (Intel·ligència artificial), Intel·ligència artificial

Subject (English)

Machine learning, Learning classifier systems, Artificial intelligence

Citation

Collections

Articles publicats en revistes (Matemàtiques i Informàtica)

Citation

MENA, José, PUJOL VILA, Oriol and VITRIÀ I MARCA, Jordi. Uncertainty-based Rejection Wrappers for Black-box Classifiers. IEEE Access. 2020. Vol. 8, num. 101721-101746. ISSN 2169-3536. [consulted: 14 of August of 2026]. Available at: https://hdl.handle.net/2445/168537

Export metadata

JSON - METS

Share record