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International Conference on Advanced Data Mining and Applications

ADMA 2009: Advanced Data Mining and Applications pp 338–349Cite as

A Hybrid Statistical Data Pre-processing Approach for Language-Independent Text Classification

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5678))

Abstract

Data pre-processing is an important topic in Text Classification (TC). It aims to convert the original textual data in a data-mining-ready structure, where the most significant text-features that serve to differentiate between text-categories are identified. Broadly speaking, textual data pre-processing techniques can be divided into three groups: (i) linguistic, (ii) statistical, and (iii) hybrid (i) & (ii). With regard to language-independent TC, our study relates to the statistical aspect only. The nature of textual data pre-processing includes: Document-base Representation (DR) and Feature Selection (FS). In this paper, we propose a hybrid statistical FS approach that integrates two existing (statistical FS) techniques, DIAAF (Darmstadt Indexing Approach Association Factor) and GSSC (Galavotti(Sebastiani(Simi Coefficient). Our proposed approach is presented under a statistical “bag of phrases” DR setting. The experimental results, based on the well-established associative text classification approach, demonstrate that our proposed technique outperforms existing mechanisms with respect to the accuracy of classification.

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Author information

Authors and Affiliations

  1. Information Management Center, China Minsheng Banking Corp., Ltd., Room 606, Building No. 8, 1 Zhongguancun Nandajie, 100873, Beijing, China

    Yanbo J. Wang

  2. Department of Computer Science, University of Liverpool, Ashton Building, Ashton Street, Liverpool, L69 3BX, UK

    Frans Coenen & Robert Sanderson

Authors
  1. Yanbo J. Wang
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  2. Frans Coenen
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  3. Robert Sanderson
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Editor information

Editors and Affiliations

  1. Knowledge Science & Engineering Institute, School of Education Technology, Beijing Normal University, Xinjiekouwai Ave. 19, 100875, Beijing, China

    Ronghuai Huang

  2. The Hong Kong University of Science and Technology, Clear Water Bay,, Hong Kong, Hong Kong

    Qiang Yang

  3. School of Computing Science, Simon Fraser University, 8888 University Drive, V5A 1S6, Burnaby, BC, Canada

    Jian Pei

  4. Faculty of Economics, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

    João Gama

  5. School of Information, Zhongguancum, Renmin University, 100872, Beijing, China

    Xiaofeng Meng

  6. School of Information Technology and Electrical Engineering, The University of Queensland, 4072, St. Lucia, Queensland, Australia

    Xue Li

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© 2009 Springer-Verlag Berlin Heidelberg

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Wang, Y.J., Coenen, F., Sanderson, R. (2009). A Hybrid Statistical Data Pre-processing Approach for Language-Independent Text Classification. In: Huang, R., Yang, Q., Pei, J., Gama, J., Meng, X., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2009. Lecture Notes in Computer Science(), vol 5678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03348-3_33

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  • DOI: https://doi.org/10.1007/978-3-642-03348-3_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03347-6

  • Online ISBN: 978-3-642-03348-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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