Stream-based Active Learning in the Presence of Label Noise
Identifieur interne : 000655 ( Main/Exploration ); précédent : 000654; suivant : 000656Stream-based Active Learning in the Presence of Label Noise
Auteurs : Mohamed-Rafik Bouguelia [France] ; Yolande Belaïd [France] ; Abdel Belaïd [France]Source :
Descripteurs français
- Wicri :
- topic : Classification.
English descriptors
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.
Url:
DOI: 10.5220/0005178900250034
Affiliations:
Links toward previous steps (curation, corpus...)
- to stream Hal, to step Corpus: 004845
- to stream Hal, to step Curation: 004845
- to stream Hal, to step Checkpoint: 000637
- to stream Main, to step Merge: 000655
- to stream Main, to step Curation: 000655
Le document en format XML
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<front><div type="abstract" xml:lang="en">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.</div>
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<tree><country name="France"><region name="Grand Est"><name sortKey="Bouguelia, Mohamed Rafik" sort="Bouguelia, Mohamed Rafik" uniqKey="Bouguelia M" first="Mohamed-Rafik" last="Bouguelia">Mohamed-Rafik Bouguelia</name>
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