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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

  1. Baker, J.E. Adaptive Selection Methods for Genetic Algorithms. In Proc. on the First International Conference on Genetic Algorithms and their applications, pp.101–111, Grefenstette, J.J. (ed). Hillsdale, New Jersey: Lawrence Earlbaum, 1985.

    Google Scholar 

  2. Dash, M and Liu, H. Feature Selection for Classification. In Intelligent Data Analysis, vol. 1, no. 3, 1997.

    Google Scholar 

  3. Holland, J.H. Adaptation in Natural and Artificial Systems. Massachusetts: MIT Press, 1992.

    Google Scholar 

  4. John, G.H., Kohavi, R. and Pfleger, K. Irrelevant Features and the Subset Selection Problem. In Proc. of the Eleventh International Conference on Machine Learning, pp.121–129. San Francisco, CA: Morgan Kauffmann Publishers, 1994.

    Google Scholar 

  5. Langley, P. Selection of Relevant Features in Machine Learning. In Proc. of the AAAI Fall Symposium on Relevance. New Orleans, LA: AAAI Press, 1994.

    Google Scholar 

  6. Salton, G. and McGill, M.J. Introduction to Modern Information Retrieval. New York: McGraw-Hill, 1983.

    Google Scholar 

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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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