Label-noise-tolerant classification for streaming data

Benoit Frénay, Barbara Hammer

Research output: Contribution in Book/Catalog/Report/Conference proceedingConference contribution

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Label noise-tolerant machine learning techniques address datasets which are affected by mislabelling of the instances. Since labelling quality is a severe issue in particular for large or streaming data sets, this setting becomes more and more relevant in the context of life-long learning, big data and crowd sourcing. In this contribution, we extend a powerful online learning method, soft robust learning vector quantisation, by a probabilistic model for noise tolerance, which is applicable for streaming data, including label-noise drift. The superiority of the technique is demonstrated in several benchmark problems.

Original languageEnglish
Title of host publication2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781509061815
ISBN (Print)9781509061815
Publication statusPublished - 30 Jun 2017
Event2017 International Joint Conference on Neural Networks, IJCNN 2017 - Anchorage, United States
Duration: 14 May 201719 May 2017

Publication series

Name2017 International Joint Conference on Neural Networks (IJCNN)


Conference2017 International Joint Conference on Neural Networks, IJCNN 2017
Country/TerritoryUnited States


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