An adaptive streaming active learning strategy based on instance weighting

Mohamed-Rafik Bouguelia 1 Yolande Belaïd 1 Belaïd Abdel 1
1 READ - Recognition of writing and analysis of documents
LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : This paper addresses stream-based active learning for classification. We propose a new query strategy based on instance weighting that improves the performance of the active learner compared to the commonly used uncertainty strategies. The proposed strategy computes the smallest weight that should be associated with new instance, so that the classifier changes its prediction regarding this instance. If a small weight is suffi- cient to change the predicted label, then the classifier was uncertain about its prediction, and the true label is queried from a labeller. In order to determine whether the sufficient weight is “small enough”, we propose an adaptive uncertainty threshold which is suitable for the streaming setting. The proposed adaptive threshold allows the stream-based active learner to achieve an accuracy which is similar to that of a fully supervised learner, while querying much less labels. Experiments on several public and real world data prove the effectiveness of the proposed method.
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Article dans une revue
Pattern Recognition Letters, Elsevier, 2016, pp.38-44
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Contributeur : Abdel Belaid <>
Soumis le : mardi 12 janvier 2016 - 13:04:38
Dernière modification le : mardi 24 avril 2018 - 13:51:32

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  • HAL Id : hal-01254510, version 1

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Mohamed-Rafik Bouguelia, Yolande Belaïd, Belaïd Abdel. An adaptive streaming active learning strategy based on instance weighting. Pattern Recognition Letters, Elsevier, 2016, pp.38-44. 〈hal-01254510〉

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