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Machine Learning by Multi-feature Extraction Using Genetic Algorithms

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Advances in Artificial Intelligence – IBERAMIA 2004 (IBERAMIA 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3315))

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Abstract

Constructive Induction methods aim to solve the problem of learning hard concepts despite complex interaction in data. We propose a new Constructive Induction method based on Genetic Algorithms with a non-algebraic representation of features. The advantage of our method to some other similar methods is that it constructs and evaluates a combination of features. Evaluating constructed features together, instead of considering them one by one, is essential when number of interacting attributes is high and there are more than one interaction in concept. Our experiments show the effectiveness of this method to learn such concepts.

This work has been partially supported by the Spanish Interdepartmental Commission for Science and Technology (CICYT), under Grant number TIC2002-1948.

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Authors and Affiliations

  1. Universidad Autónoma de Madrid, E–28049, Madrid, Spain

    Leila S. Shafti & Eduardo Pérez

Authors
  1. Leila S. Shafti
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  2. Eduardo Pérez
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Editor information

Editors and Affiliations

  1. Laboratorio Nacional de Informatica Avanzada, A.C. Lania, Rebsamen 80, Col. Centro, 91000, Xalapa, Veracruz, Mexico

    Christian Lemaître

  2. Department of Computer Science, National Institute of Astrophysics, Optics and Electronics (INAOE), Luis E. Erro 1, 72840, Sta. Maria Tonantzintla, Puebla, Mexico

    Carlos A. Reyes

  3. Computer Science Department, National Institute of Astrophysics, Optics and Electronics, Luis Enrique Erro 1, 72840, Tonantzintla, México

    Jesús A. González

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Shafti, L.S., Pérez, E. (2004). Machine Learning by Multi-feature Extraction Using Genetic Algorithms. In: Lemaître, C., Reyes, C.A., González, J.A. (eds) Advances in Artificial Intelligence – IBERAMIA 2004. IBERAMIA 2004. Lecture Notes in Computer Science(), vol 3315. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30498-2_25

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  • DOI: https://doi.org/10.1007/978-3-540-30498-2_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23806-5

  • Online ISBN: 978-3-540-30498-2

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