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Applying genetic algorithms to the feature selection problem in information retrieval

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Flexible Query Answering Systems (FQAS 1998)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1495))

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Abstract

The demand of accuracy and speed in the Information Retrieval processes has revealed the necessity of a good classification of the large collection of documents existing in databases and Web servers. The representation of documents in the vector space model with terms as features offers the possibility of application of Machine Learning techniques. A filter method to select the most relevant features before the classification process is presented in this paper. A Genetic Algorithm (GA) is used as a powerful tool to search solutions in the domain of relevant features. Implementation and some preliminary experiments have been realized. The application of this technique to the vector space model in Information Retrieval is outlined as future work.

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References

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Author information

Authors and Affiliations

  1. Department of Computer Science and Artificial Intelligence, Granada University, Avda. Andalucía s/n, 18071, Granada, Spain

    María J. Martín-Bautista & María-Amparo Vila

Authors
  1. María J. Martín-Bautista
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  2. María-Amparo Vila
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Editor information

Troels Andreasen Henning Christiansen Henrik Legind Larsen

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© 1998 Springer-Verlag Berlin Heidelberg

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Martín-Bautista, M.J., Vila, MA. (1998). Applying genetic algorithms to the feature selection problem in information retrieval. In: Andreasen, T., Christiansen, H., Larsen, H.L. (eds) Flexible Query Answering Systems. FQAS 1998. Lecture Notes in Computer Science, vol 1495. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0056008

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  • DOI: https://doi.org/10.1007/BFb0056008

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65082-9

  • Online ISBN: 978-3-540-49655-7

  • eBook Packages: Springer Book Archive

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