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Learning Effective XML Classifiers Based on Discriminatory Structures and Nested Content

  • Conference paper
Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2011)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 348))

Abstract

Supervised classification aims to learn a model (or a classifier) from a collection of XML documents individually marked with one of a predefined set of class labels. The learnt classifier isolates each class by the content and structural regularities observed within the respective labeled XML documents and, thus, allows to predict the unknown class of unlabeled XML documents by looking at their content and structural features. The classification of unlabeled XML documents into the predefined classes is a valuable support for more effective and efficient XML search, retrieval and filtering.

We discuss an approach for learning intelligible XML classifiers. XML documents are represented as transactions in a space of boolean features, that are informative of their content and structure. Learning algorithms induce compact associative classifiers with outperforming effectiveness from the transactional XML representation. A preprocessing step contributes to the scalability of the approach with the size of XML corpora.

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Author information

Authors and Affiliations

  1. ICAR-CNR, I87036, Rende, CS, Italy

    Gianni Costa, Riccardo Ortale & Ettore Ritacco

Authors
  1. Gianni Costa
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  2. Riccardo Ortale
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  3. Ettore Ritacco
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Editor information

Editors and Affiliations

  1. IST - Technical University of Lisbon, Av.Rovisco Pais, 1, 1049-001, Lisbon, Portugal

    Ana Fred

  2. Delft University of Technology, Mekelweg 4, 2628 CD, Delft, The Netherlands

    Jan L. G. Dietz

  3. Informatics Research Centre, Henley Business School, University of Reading, RG6 6UD, Reading, UK

    Kecheng Liu

  4. INSTICC and IPS, Estefanilha, Setúbal, Portugal

    Joaquim Filipe

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Costa, G., Ortale, R., Ritacco, E. (2013). Learning Effective XML Classifiers Based on Discriminatory Structures and Nested Content. In: Fred, A., Dietz, J.L.G., Liu, K., Filipe, J. (eds) Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2011. Communications in Computer and Information Science, vol 348. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37186-8_10

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  • DOI: https://doi.org/10.1007/978-3-642-37186-8_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37185-1

  • Online ISBN: 978-3-642-37186-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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