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Agreeing to disagree: active learning with noisy labels without crowdsourcing
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-2859-6155
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-7796-5201
The University of South Dakota, Vermillion, South Dakota, USA.ORCID iD: 0000-0003-4176-0236
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0003-2185-8973
2017 (English)In: International Journal of Machine Learning and Cybernetics, ISSN 1868-8071, E-ISSN 1868-808XArticle in journal (Refereed) Epub ahead of print
Abstract [en]

We propose a new active learning method for classification, which handles label noise without relying on multiple oracles (i.e., crowdsourcing). We propose a strategy that selects (for labeling) instances with a high influence on the learned model. An instance x is said to have a high influence on the model h, if training h on x (with label y = h(x)) would result in a model that greatly disagrees with h on labeling other instances. Then, we propose another strategy that selects (for labeling) instances that are highly influenced by changes in the learned model. An instance x is said to be highly influenced, if training h with a set of instances would result in a committee of models that agree on a common label for x but disagree with h(x). We compare the two strategies and we show, on different publicly available datasets, that selecting instances according to the first strategy while eliminating noisy labels according to the second strategy, greatly improves the accuracy compared to several benchmarking methods, even when a significant amount of instances are mislabeled. © Springer-Verlag Berlin Heidelberg 2017

Place, publisher, year, edition, pages
Heidelberg: Springer, 2017.
Keyword [en]
Active learning, Classification, Label noise, Mislabeling, Interactive learning, Machine learning, Data mining
National Category
Signal Processing Computer Systems Computer Science
Identifiers
URN: urn:nbn:se:hh:diva-33365DOI: 10.1007/s13042-017-0645-0OAI: oai:DiVA.org:hh-33365DiVA: diva2:1077485
Available from: 2017-02-27 Created: 2017-02-27 Last updated: 2017-12-06Bibliographically approved

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Bouguelia, Mohamed-RafikNowaczyk, SławomirVerikas, Antanas

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