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Self-organizing cases to find paradigms

  • Plasticity Phenomena (Maturing, Learning & Memory)
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Foundations and Tools for Neural Modeling (IWANN 1999)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1606))

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Abstract

Case-based information systems can be seen as lazy machine learning algorithms; they select a number of training instances and then classify unseen cases as the most similar stored instance. One of the main disadvantages of these systems is the high number of patterns retained. In this paper, a new method for extracting just a small set of paradigms from a set of training examples is presented. Additionally, we provide the set of attributes describing the representative examples that are relevant for classification purposes. Our algorithm computes the Kohonen self-organizing maps attached to the training set to then compute the coverage of each map node. Finally, a heuristic procedure selects both the paradigms and the dimensions (or attributes) to be considered when measuring similarity in future classification tasks.

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Authors and Affiliations

  1. Centro de Inteligencia Artificial, Universidad de Oviedo at Gijón, Campus de Viesques, E-33271, Gijón, España

    Juan José del Coz, Oscar Luaces, José Ramón Quevedo, Jaime Alonso, José Ranilla & Antonio Bahamonde

Authors
  1. Juan José del Coz
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  2. Oscar Luaces
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  3. José Ramón Quevedo
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  4. Jaime Alonso
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  5. José Ranilla
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  6. Antonio Bahamonde
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Editor information

José Mira Juan V. Sánchez-Andrés

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© 1999 Springer-Verlag Berlin Heidelberg

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José del Coz, J., Luaces, O., Quevedo, J.R., Alonso, J., Ranilla, J., Bahamonde, A. (1999). Self-organizing cases to find paradigms. In: Mira, J., Sánchez-Andrés, J.V. (eds) Foundations and Tools for Neural Modeling. IWANN 1999. Lecture Notes in Computer Science, vol 1606. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0098210

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  • DOI: https://doi.org/10.1007/BFb0098210

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66069-9

  • Online ISBN: 978-3-540-48771-5

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