{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T14:53:51Z","timestamp":1774968831368,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,28]],"date-time":"2022-01-28T00:00:00Z","timestamp":1643328000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"COMPETE 2020 (Operational Programme Competitiveness and Internationalization) and European Regional Development Fund (ERDF)","award":["POCI-01-0247-FEDER-033479"],"award-info":[{"award-number":["POCI-01-0247-FEDER-033479"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Electronics"],"abstract":"<jats:p>Uncertainty is present in every single prediction of Machine Learning (ML) models. Uncertainty Quantification (UQ) is arguably relevant, in particular for safety-critical applications. Prior research focused on the development of methods to quantify uncertainty; however, less attention has been given to how to leverage the knowledge of uncertainty in the process of model development. This work focused on applying UQ into practice, closing the gap of its utility in the ML pipeline and giving insights into how UQ is used to improve model development and its interpretability. We identified three main research questions: (1) How can UQ contribute to choosing the most suitable model for a given classification task? (2) Can UQ be used to combine different models in a principled manner? (3) Can visualization techniques improve UQ\u2019s interpretability? These questions are answered by applying several methods to quantify uncertainty in both a simulated dataset and a real-world dataset of Human Activity Recognition (HAR). Our results showed that uncertainty quantification can increase model robustness and interpretability.<\/jats:p>","DOI":"10.3390\/electronics11030396","type":"journal-article","created":{"date-parts":[[2022,1,29]],"date-time":"2022-01-29T01:41:59Z","timestamp":1643420519000},"page":"396","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Uncertainty-Based Rejection in Machine Learning: Implications for Model Development and Interpretability"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9445-4809","authenticated-orcid":false,"given":"Mar\u00edlia","family":"Barandas","sequence":"first","affiliation":[{"name":"Associa\u00e7\u00e3o Fraunhofer Portugal Research, Rua Alfredo Allen 455\/461, 4200-135 Porto, Portugal"},{"name":"Laborat\u00f3rio de Instrumenta\u00e7\u00e3o, Engenharia Biom\u00e9dica e F\u00edsica da Radia\u00e7\u00e3o (LIBPhys-UNL), Departamento de F\u00edsica, Faculdade de Ci\u00eancias e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8481-6079","authenticated-orcid":false,"given":"Duarte","family":"Folgado","sequence":"additional","affiliation":[{"name":"Associa\u00e7\u00e3o Fraunhofer Portugal Research, Rua Alfredo Allen 455\/461, 4200-135 Porto, Portugal"},{"name":"Laborat\u00f3rio de Instrumenta\u00e7\u00e3o, Engenharia Biom\u00e9dica e F\u00edsica da Radia\u00e7\u00e3o (LIBPhys-UNL), Departamento de F\u00edsica, Faculdade de Ci\u00eancias e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4478-2476","authenticated-orcid":false,"given":"Ricardo","family":"Santos","sequence":"additional","affiliation":[{"name":"Associa\u00e7\u00e3o Fraunhofer Portugal Research, Rua Alfredo Allen 455\/461, 4200-135 Porto, Portugal"},{"name":"Laborat\u00f3rio de Instrumenta\u00e7\u00e3o, Engenharia Biom\u00e9dica e F\u00edsica da Radia\u00e7\u00e3o (LIBPhys-UNL), Departamento de F\u00edsica, Faculdade de Ci\u00eancias e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1678-5709","authenticated-orcid":false,"given":"Raquel","family":"Sim\u00e3o","sequence":"additional","affiliation":[{"name":"Laborat\u00f3rio de Instrumenta\u00e7\u00e3o, Engenharia Biom\u00e9dica e F\u00edsica da Radia\u00e7\u00e3o (LIBPhys-UNL), Departamento de F\u00edsica, Faculdade de Ci\u00eancias e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4022-7424","authenticated-orcid":false,"given":"Hugo","family":"Gamboa","sequence":"additional","affiliation":[{"name":"Associa\u00e7\u00e3o Fraunhofer Portugal Research, Rua Alfredo Allen 455\/461, 4200-135 Porto, Portugal"},{"name":"Laborat\u00f3rio de Instrumenta\u00e7\u00e3o, Engenharia Biom\u00e9dica e F\u00edsica da Radia\u00e7\u00e3o (LIBPhys-UNL), Departamento de F\u00edsica, Faculdade de Ci\u00eancias e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Cobb, A.D., Jalaian, B., Bastian, N.D., and Russell, S. (2021). Toward Safe Decision-Making via Uncertainty Quantification in Machine Learning. Systems Engineering and Artificial Intelligence, Springer.","DOI":"10.1007\/978-3-030-77283-3_19"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.ins.2013.07.030","article-title":"Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty","volume":"255","author":"Senge","year":"2014","journal-title":"Inf. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41746-020-00367-3","article-title":"Second opinion needed: Communicating uncertainty in medical machine learning","volume":"4","author":"Kompa","year":"2021","journal-title":"NPJ Digit. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1007\/s10994-021-05946-3","article-title":"Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods","volume":"110","author":"Waegeman","year":"2021","journal-title":"Mach. Learn."},{"key":"ref_5","unstructured":"Huang, Z., Lam, H., and Zhang, H. (2021). Quantifying Epistemic Uncertainty in Deep Learning. arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e1312","DOI":"10.1002\/widm.1312","article-title":"Causability and explainability of artificial intelligence in medicine","volume":"9","author":"Holzinger","year":"2019","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Nguyen, V.L., Shaker, M.H., and H\u00fcllermeier, E. (2021). How to measure uncertainty in uncertainty sampling for active learning. Mach. Learn., 1\u201334.","DOI":"10.1007\/s10994-021-06003-9"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Bota, P., Silva, J., Folgado, D., and Gamboa, H. (2019). A semi-automatic annotation approach for human activity recognition. Sensors, 19.","DOI":"10.3390\/s19030501"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ghosh, S., Liao, Q.V., Ramamurthy, K.N., Navratil, J., Sattigeri, P., Varshney, K.R., and Zhang, Y. (2021). Uncertainty Quantification 360: A Holistic Toolkit for Quantifying and Communicating the Uncertainty of AI. arXiv.","DOI":"10.1145\/3493700.3493767"},{"key":"ref_10","unstructured":"Chung, Y., Char, I., Guo, H., Schneider, J., and Neiswanger, W. (2021). Uncertainty toolbox: An open-source library for assessing, visualizing, and improving uncertainty quantification. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10916-021-01783-y","article-title":"Machine Learning for Health: Algorithm Auditing & Quality Control","volume":"45","author":"Oala","year":"2021","journal-title":"J. Med. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"385","DOI":"10.3233\/IDA-2009-0371","article-title":"An overview of advances in reliability estimation of individual predictions in machine learning","volume":"13","author":"Kononenko","year":"2009","journal-title":"Intell. Data Anal."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Tornede, A., Gehring, L., Tornede, T., Wever, M., and H\u00fcllermeier, E. (2021). Algorithm selection on a meta level. arXiv.","DOI":"10.1007\/s10994-022-06161-4"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1427","DOI":"10.1109\/TVCG.2020.3030354","article-title":"Explainable Matrix-Visualization for Global and Local Interpretability of Random Forest Classification Ensembles","volume":"27","author":"Neto","year":"2020","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Shaker, M.H., and H\u00fcllermeier, E. (2021). Ensemble-based Uncertainty Quantification: Bayesian versus Credal Inference. arXiv.","DOI":"10.58895\/ksp\/1000138532-5"},{"key":"ref_16","unstructured":"Malinin, A., Prokhorenkova, L., and Ustimenko, A. (2020). Uncertainty in gradient boosting via ensembles. arXiv."},{"key":"ref_17","unstructured":"Depeweg, S., Hernandez-Lobato, J.M., Doshi-Velez, F., and Udluft, S. (2018, January 10\u201315). Decomposition of uncertainty in Bayesian deep learning for efficient and risk-sensitive learning. Proceedings of the International Conference on Machine Learning, Stockholm, Sweden."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Shaker, M.H., and H\u00fcllermeier, E. (2020). Aleatoric and epistemic uncertainty with random forests. arXiv.","DOI":"10.1007\/978-3-030-44584-3_35"},{"key":"ref_19","first-page":"54","article-title":"Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy","volume":"1","author":"Efron","year":"1986","journal-title":"Stat. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Stracuzzi, D.J., Darling, M.C., Peterson, M.G., and Chen, M.G. (2018). Quantifying Uncertainty to Improve Decision Making in Machine Learning, Technical Report.","DOI":"10.2172\/1481629"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"101721","DOI":"10.1109\/ACCESS.2020.2996495","article-title":"Uncertainty-based rejection wrappers for black-box classifiers","volume":"8","author":"Mena","year":"2020","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3614","DOI":"10.1109\/TPAMI.2020.2981604","article-title":"Recent advances in open set recognition: A survey","volume":"43","author":"Geng","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Perello-Nieto, M., Telmo De Menezes Filho, E.S., Kull, M., and Flach, P. (2016, January 12\u201315). Background Check: A general technique to build more reliable and versatile classifiers. Proceedings of the 2016 IEEE 16th International Conference on Data Mining (ICDM), Barcelona, Spain.","DOI":"10.1109\/ICDM.2016.0150"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"505","DOI":"10.3390\/make2040028","article-title":"Towards Knowledge Uncertainty Estimation for Open Set Recognition","volume":"2","author":"Pires","year":"2020","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1109\/TIT.1970.1054406","article-title":"On optimum recognition error and reject tradeoff","volume":"16","author":"Chow","year":"1970","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1565","DOI":"10.1016\/j.patrec.2008.03.010","article-title":"Growing a multi-class classifier with a reject option","volume":"29","author":"Tax","year":"2008","journal-title":"Pattern Recognit. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2099","DOI":"10.1016\/S0031-3203(00)00059-5","article-title":"Reject option with multiple thresholds","volume":"33","author":"Fumera","year":"2000","journal-title":"Pattern Recognit."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"106984","DOI":"10.1016\/j.patcog.2019.106984","article-title":"Performance visualization spaces for classification with rejection option","volume":"96","author":"Hanczar","year":"2019","journal-title":"Pattern Recognit."},{"key":"ref_29","unstructured":"Franc, V., Prusa, D., and Voracek, V. (2021). Optimal strategies for reject option classifiers. arXiv."},{"key":"ref_30","unstructured":"Charoenphakdee, N., Cui, Z., Zhang, Y., and Sugiyama, M. (2021, January 13\u201315). Classification with rejection based on cost-sensitive classification. Proceedings of the International Conference on Machine Learning, Virtual."},{"key":"ref_31","unstructured":"Gal, Y. (2016). Uncertainty in Deep Learning. [Ph.D. Dissertation, University of Cambridge]."},{"key":"ref_32","unstructured":"Nadeem, M.S.A., Zucker, J.D., and Hanczar, B. (2009, January 5\u20136). Accuracy-rejection curves (ARCs) for comparing classification methods with a reject option. Proceedings of the third International Workshop on Machine Learning in Systems Biology, Ljubljana, Slovenia."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1016\/j.patcog.2016.10.011","article-title":"Performance measures for classification systems with rejection","volume":"63","author":"Condessa","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_34","unstructured":"Kl\u00e4s, M. (2018). Towards identifying and managing sources of uncertainty in AI and machine learning models-an overview. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Campagner, A., Cabitza, F., and Ciucci, D. (2020). Three-way decision for handling uncertainty in machine learning: A narrative review. International Joint Conference on Rough Sets, Springer.","DOI":"10.1007\/978-3-030-52705-1_10"},{"key":"ref_36","unstructured":"Sambyal, A.S., Krishnan, N.C., and Bathula, D.R. (2021). Towards Reducing Aleatoric Uncertainty for Medical Imaging Tasks. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1016\/j.neucom.2016.06.038","article-title":"Optimal local rejection for classifiers","volume":"214","author":"Fischer","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_38","unstructured":"Dua, D., and Graff, C. (2019). UCI Machine Learning Repository, University of California, School of Information and Computer Science. Available online: http:\/\/archive.ics.uci.edu\/ml."},{"key":"ref_39","first-page":"3","article-title":"A public domain dataset for human activity recognition using smartphones","volume":"3","author":"Anguita","year":"2013","journal-title":"Esann"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Buckley, C., Alcock, L., McArdle, R., Rehman, R.Z.U., Del Din, S., Mazz\u00e0, C., Yarnall, A.J., and Rochester, L. (2019). The role of movement analysis in diagnosing and monitoring neurodegenerative conditions: Insights from gait and postural control. Brain Sci., 9.","DOI":"10.3390\/brainsci9020034"}],"container-title":["Electronics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-9292\/11\/3\/396\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:09:57Z","timestamp":1760134197000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-9292\/11\/3\/396"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,28]]},"references-count":40,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["electronics11030396"],"URL":"https:\/\/doi.org\/10.3390\/electronics11030396","relation":{},"ISSN":["2079-9292"],"issn-type":[{"value":"2079-9292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,28]]}}}