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Pattern Extraction Method for Text Classification

  • Hung Son Nguyen
  • Hui Wang
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 89)

Abstract

The quality of classification can be increased by using some feature extraction algorithm, i.e. the algorithm that finds new and more relevant features, before application of learning procedure. In this paper, we investigate a novel feature extraction method for textual data. Usually, texts (documents) are represented as collections of words or keywords. We present a method for finding new numerical attributes that improve the quality of classification. New features are based on a set of words (text pattern) and are defined as number of words occurring in both text pattern and the considered document. Our approach is based on Rough set methods and Lattice Machine theory. The experimental results show that the presented methods improve the classification quality on almost all textual data.

Keywords

Text Classification Textual Data Decision Table Pattern Text Pattern Extraction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Hung Son Nguyen
    • 1
  • Hui Wang
    • 2
  1. 1.Institute of MathematicsWarsaw UniversityWarsawPoland
  2. 2.School of Information and SoftwareEngineering University of Ulster at Jordanstown NIreland

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