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Robust constructive induction

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KI-94: Advances in Artificial Intelligence (KI 1994)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 861))

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Abstract

We describe how CiPF 2.0, a propositional constructive learner, can cope with both noise and representation mismatch in training examples simultaneously. CiPF 2.0 abilities stem from coupling the robust selective learner C4.5 with a sophisticated constructive induction component. An important new constructive operator incorporated into CiPF 2.0 is the simplified Kramer operator abstracting combinations of two attributes into a single new boolean attribute. The so-called Minimum Description Length (MDL) principle acts as a powerful control heuristic guiding search in the representation space through the abundance of opportunities for constructively adding new attributes. Claims are confirmed empirically by experiments in two artificial domains.

Financial support for the Austrian Research Institute for Artificial Intelligence is provided by the Austrian Federal Ministry of Science and Research. I would like to thank Gerhard Widmer for constructive discussion and help with this paper, and Johannes Fürnkranz for providing the king-rook-king position generator.

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Authors and Affiliations

  1. Austrian Research Institute for Artificial Intelligence, Schottengasse 3, A-1010, Vienna, Austria

    Bernhard Pfahringer

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  1. Bernhard Pfahringer
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Bernhard Nebel Leonie Dreschler-Fischer

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© 1994 Springer-Verlag Berlin Heidelberg

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Pfahringer, B. (1994). Robust constructive induction. In: Nebel, B., Dreschler-Fischer, L. (eds) KI-94: Advances in Artificial Intelligence. KI 1994. Lecture Notes in Computer Science, vol 861. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58467-6_11

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  • DOI: https://doi.org/10.1007/3-540-58467-6_11

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-58467-4

  • Online ISBN: 978-3-540-48979-5

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