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The Generic Rough Set Inductive Logic Programming Model and Motifs in Strings

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New Directions in Rough Sets, Data Mining, and Granular-Soft Computing (RSFDGrC 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1711))

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Abstract

The gRS-ILP model (generic Rough Set Inductive Logic Programming model) provides a framework for Inductive Logic Programming when the setting is imprecise and any induced logic program will not be able to distinguish between certain positive and negative examples. However, in this rough setting, where it is inherently not possible to describe the entire data with 100% accuracy, it is possible to definitively describe part of the data with 100% accuracy. The gRS-ILP model is extended in this paper to motifs in strings. An illustrative experiment is presented using the ILP system Progol on transmembrane domains in amino acid sequences.

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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, Yamaguchi University, Ube, 755-8611, Japan

    K. Inoue

Authors
  1. Arul Siromoney
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  2. K. Inoue
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Editor information

Editors and Affiliations

  1. The International WIC Institute, Beijing University of Technology, China

    Ning Zhong

  2. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron

  3. Professor Emeritus, University of Tokyo,  

    Setsuo Ohsuga

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© 1999 Springer-Verlag Berlin Heidelberg

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Siromoney, A., Inoue, K. (1999). The Generic Rough Set Inductive Logic Programming Model and Motifs in Strings. In: Zhong, N., Skowron, A., Ohsuga, S. (eds) New Directions in Rough Sets, Data Mining, and Granular-Soft Computing. RSFDGrC 1999. Lecture Notes in Computer Science(), vol 1711. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-48061-7_20

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  • DOI: https://doi.org/10.1007/978-3-540-48061-7_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66645-5

  • Online ISBN: 978-3-540-48061-7

  • eBook Packages: Springer Book Archive

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