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International Workshop on Fundamentals of Artificial Intelligence Research

FAIR 1991: Fundamentals of Artificial Intelligence Research pp 99–111Cite as

On the phenomenon of flattening “flexible prediction” concept hierarchy

  • Part II Selected Contributions
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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 535))

Abstract

The Incremental Concept Formation method, as described in [19] is claimed therein to be “able to formulate diagnostically useful categories even without class information”, “given real world data on heart disease”. We suggest in this paper that the method does not derive categories from the data but from primary and derived attribute selection by showing that equal treatment of all attributes leads to a flat (one level) concept hierarchy.

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Authors and Affiliations

  1. Institute of Computer Science, Polish Academy of Sciences, P.O.Box 22, 00-901, Warsaw, Poland

    Mieczyslaw A. Klopotek

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  1. Mieczyslaw A. Klopotek
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Philippe Jorrand Jozef Kelemen

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© 1991 Springer-Verlag Berlin Heidelberg

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Klopotek, M.A. (1991). On the phenomenon of flattening “flexible prediction” concept hierarchy. In: Jorrand, P., Kelemen, J. (eds) Fundamentals of Artificial Intelligence Research. FAIR 1991. Lecture Notes in Computer Science, vol 535. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-54507-7_9

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  • DOI: https://doi.org/10.1007/3-540-54507-7_9

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-54507-1

  • Online ISBN: 978-3-540-38420-5

  • eBook Packages: Springer Book Archive

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