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Measures of Ruleset Quality Capable to Represent Uncertain Validity

  • Conference paper
Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4724))

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Abstract

The paper deals with quality measures of rules extracted from data, more precisely with measures of the whole extracted rulesets. Three particular approaches to extending ruleset quality measures from classification to general rulesets are discussed, and one of them, capable to represent uncertain validity of rulesets for objects, is elaborated in some detail. In particular, a generalization of ROC curves is proposed. The approach is illustrated on rulesets extracted with four important methods from the well-known iris data.

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Author information

Authors and Affiliations

  1. Institute of Computer Science, Academy of Sciences of the Czech Republic, Pod vodárenskou věží 2, 18207, Praha 8, Czech Republic

    Martin Holeňa

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  1. Martin Holeňa
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Editor information

Editors and Affiliations

  1. LARODEC, Institut Supérieur de Gestion de Tunis, 41 Avenue de la liberté, 2000, Le Bardo, Tunisie

    Khaled Mellouli

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Holeňa, M. (2007). Measures of Ruleset Quality Capable to Represent Uncertain Validity. In: Mellouli, K. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2007. Lecture Notes in Computer Science(), vol 4724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75256-1_39

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  • DOI: https://doi.org/10.1007/978-3-540-75256-1_39

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-75255-4

  • Online ISBN: 978-3-540-75256-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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