Abstract : Mislabelling is a critical problem for stream-based active learning methods because it not only impacts the classification accuracy but also deviates the active learner from querying informative data. Dealing with label noise is omitted by most existing active learning methods. We address this issue and propose an efficient method to identify and mitigate mislabelling errors for active learning in the streaming setting. We first propose a mislabelling likelihood measure to characterize the potentially mislabelled instances. This measure is based on the degree of disagreement among the predicted and the queried class label (given by the labeller). Then, we derive a measure of informativeness that expresses how much the label of an instance needs to be corrected by an expert labeller. Specifically, an instance is worth relabelling if it shows highly conflicting information among the predicted and the queried labels. We show that filtering instances with a high mislabelling likelihood and correcting only the filtered instances with a high conflicting information greatly improves the performances of the active learner. Experiments on several real world data prove the effectiveness of the proposed method in terms of filtering efficiency and classification accuracy of the stream-based active learner.
https://hal.inria.fr/hal-01116114
Contributeur : Yolande Belaid
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Soumis le : jeudi 12 février 2015 - 15:32:46
Dernière modification le : mardi 24 avril 2018 - 13:30:42
Document(s) archivé(s) le : dimanche 16 avril 2017 - 08:29:31
Mohamed-Rafik Bouguelia, Yolande Belaïd, Abdel Belaïd. Stream-based Active Learning in the Presence of Label Noise. 4th International Conference on Pattern Recognition Applications and Methods - ICPRAM 2015, Jan 2015, Lisbon, Portugal. SciTePress, pp.25 - 34, 2015, 〈http://www.icpram.org/Home.aspx?y=2015〉. 〈10.5220/0005178900250034〉. 〈hal-01116114〉
