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Classification de flux de documents évolutifs avec apprentissage de classes inconnues

Mohamed-Rafik Bouguelia 1 Yolande Belaïd 1 Abdel Belaïd 1
1 READ - Recognition of writing and analysis of documents
LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : In this paper, we propose a stream-based semi-supervised active learning method for document classification, which is able to query (from an operator) the class labels of documents that are informative, according to an uncertainty measure. The method maintains a dynamically evolving graph topology of labelled document-representatives, which constitutes a covered feature space. The method is able to automatically discover the emergence of novel classes in the stream. An incoming document is identified as a member of a novel class or an existing class, depending on whether it is outside or inside the area covered by the known classes. Experiments on different real datasets show that the proposed method requires a small amount of the incoming documents to be labelled, in order to learn a model which achieves better or equal accuracy than to the usual supervised methods with fully labelled training documents.
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Contributor : Yolande Belaid <>
Submitted on : Friday, March 13, 2015 - 4:00:45 PM
Last modification on : Friday, January 15, 2021 - 5:42:02 PM


  • HAL Id : hal-01131453, version 1



Mohamed-Rafik Bouguelia, Yolande Belaïd, Abdel Belaïd. Classification de flux de documents évolutifs avec apprentissage de classes inconnues. Document Numérique, Lavoisier, 2014, De l'imprimé au multimodal - Analyse et reconnaissance du document numérique, 17 (3), pp.21. ⟨hal-01131453⟩



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