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Congress of the Italian Association for Artificial Intelligence

AI*IA 1997: AI*IA 97: Advances in Artificial Intelligence pp 24–35Cite as

Handling continuous data in top-down induction of first-order rules

  • Machine Learning 1
  • Conference paper
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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1321))

Abstract

Handling numerical information is one of the most important research issues for practical applications of first-order learning systems. This paper is concerned with the problem of inducing first-order classification rules from both numeric and symbolic data. We propose a specialization operator that discretizes continuous data during the learning process. The heuristic function used to choose among different discretizations satisfies a property that can be profitably exploited to improve the efficiency of the specialization operator. The operator has been implemented and bested on the document understanding domain.

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Author information

Authors and Affiliations

  1. Dipartimento di Informatica, Università degli Studi di Bari, via Orabona 4, 70125, Bari, Italy

    Donato Malerba, Floriana Esposito, Giovanni Semeraro & Sergio Caggese

Authors
  1. Donato Malerba
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  2. Floriana Esposito
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  3. Giovanni Semeraro
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  4. Sergio Caggese
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Editor information

Maurizio Lenzerini

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© 1997 Springer-Verlag Berlin Heidelberg

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Malerba, D., Esposito, F., Semeraro, G., Caggese, S. (1997). Handling continuous data in top-down induction of first-order rules. In: Lenzerini, M. (eds) AI*IA 97: Advances in Artificial Intelligence. AI*IA 1997. Lecture Notes in Computer Science, vol 1321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63576-9_93

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  • DOI: https://doi.org/10.1007/3-540-63576-9_93

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63576-5

  • Online ISBN: 978-3-540-69601-8

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