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Stream-based Active Learning in the Presence of Label Noise
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.
Cited literature [21 references]
https://hal.inria.fr/hal-01116114
Contributor : Yolande Belaid Connect in order to contact the contributor
Submitted on : Thursday, February 12, 2015 - 3:32:46 PM
Last modification on : Saturday, October 16, 2021 - 11:26:09 AM
Long-term archiving on: : Sunday, April 16, 2017 - 8:29:31 AM
Contributor : Yolande Belaid Connect in order to contact the contributor
Submitted on : Thursday, February 12, 2015 - 3:32:46 PM
Last modification on : Saturday, October 16, 2021 - 11:26:09 AM
Long-term archiving on: : Sunday, April 16, 2017 - 8:29:31 AM
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