TY - GEN
T1 - Label-noise-tolerant classification for streaming data
AU - Frénay, Benoit
AU - Hammer, Barbara
N1 - Funding Information:
Acknowledgement: BH gratefully acknowledges funding from the CITEC centre of excellence and the leading edge cluster it’s owl. BF initiated this work when he was at Université catholique de Louvain.
Publisher Copyright:
© 2017 IEEE.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2017/6/30
Y1 - 2017/6/30
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85030979635&partnerID=8YFLogxK
UR - https://researchportal.unamur.be/en/publications/labelnoisetolerant-classification-for-streaming-data(6168a89c-7b66-47e8-8994-dfe6cf1ef287).html
U2 - 10.1109/ijcnn.2017.7966062
DO - 10.1109/ijcnn.2017.7966062
M3 - Conference contribution
AN - SCOPUS:85030979635
SN - 9781509061815
VL - 2017-May
T3 - 2017 International Joint Conference on Neural Networks (IJCNN)
SP - 1748
EP - 1755
BT - 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 International Joint Conference on Neural Networks, IJCNN 2017
Y2 - 14 May 2017 through 19 May 2017
ER -