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The Generic Rough Set Inductive Logic Programming (gRS—ILP) Model

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Book cover Data Mining, Rough Sets and Granular Computing

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 95))

Abstract

The example semantics of Inductive Logic Programming (ILP) systems is said to be in a rough setting when the consistency and completeness criteria cannot both be fulfilled together, because the evidence, background knowledge and declarative bias are such that any induced hypothesis cannot distinguish between some of the positive and negative examples. The gRS-ILP model (generic Rough Set Inductive Logic Programming model) provides a theoretical foundation in this rough setting for an ILP system to induce hypotheses that are used to say that an example is definitely positive, or definitely negative. An illustrative example using Progol is presented. Results are presented of GOLEM experiments using the data set for drug design for Alzheimer’s disease and other experiments using Progol on mutagenesis data and transmembrane domain data.

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Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Anna University, Chennai, 600 025, India

    Arul Siromoney

  2. Department of Computer Science and Systems Engineering, Faculty of Engineering, Yamaguchi University, Ube, 755-8611, Japan

    Katsushi Inoue

Authors
  1. Arul Siromoney
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  2. Katsushi Inoue
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Editor information

Editors and Affiliations

  1. Department of Mathematics and Computer Science, San Jose State University The Metropolitan University of Silicon Valley, One Washington Square, 95192-0103, San Jose, CA, USA

    Tsau Young Lin

  2. Department of Computer Science, University of Regina, S4S 0A2, Regina, Saskatchewan, Canada

    Yiyu Y. Yao

  3. Computer Science Division and Electronics Research Laboratory Department of Electrical and Electronics, University of California Berkeley Initiative in Soft Computing (BISC), 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

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© 2002 Springer-Verlag Berlin Heidelberg

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Siromoney, A., Inoue, K. (2002). The Generic Rough Set Inductive Logic Programming (gRS—ILP) Model. In: Lin, T.Y., Yao, Y.Y., Zadeh, L.A. (eds) Data Mining, Rough Sets and Granular Computing. Studies in Fuzziness and Soft Computing, vol 95. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1791-1_25

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  • DOI: https://doi.org/10.1007/978-3-7908-1791-1_25

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-2508-4

  • Online ISBN: 978-3-7908-1791-1

  • eBook Packages: Springer Book Archive

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