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Rough Sets and Relational Learning

  • Conference paper
Transactions on Rough Sets I

Part of the book series: Lecture Notes in Computer Science ((TRS,volume 3100))

Abstract

Rough Set Theory is a mathematical tool to deal with vagueness and uncertainty. Rough Set Theory uses a single information table. Relational Learning is the learning from multiple relations or tables. Recent research in Rough Set Theory includes the extension of Rough Set Theory to Relational Learning. A brief overview of the work in Rough Sets and Relational Learning is presented.

The authors’ work in this area is then presented. Inductive Logic Programming (ILP) is one of the main approaches to Relational Learning. The generic Rough Set Inductive Logic Programming model introduces a rough setting in ILP. The Variable Precision Rough Set Inductive Logic Programming model (VPRSILP model) extends the Variable Precision Rough Set model to ILP.

In the cVPRSILP approach based on the VPRSILP model, elementary sets are defined using attributes that are based on a finite number of clauses of interest. However, this results in the number of elementary sets being very large. So, only significant elementary sets are used, and test cases are classified based on their proximity to the significant elementary sets.

The utility of this approach is shown in classification experiments in predictive toxicology.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Madras Christian College, Chennai, 600 059, India

    R. S. Milton

  2. Department of Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai, 600 025, India

    V. Uma Maheswari & Arul Siromoney

Authors
  1. R. S. Milton
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  2. V. Uma Maheswari
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  3. Arul Siromoney
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Manitoba, R3T 5V6, Winnipeg, Manitoba, Canada

    James F. Peters

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

    Andrzej Skowron  & Marcin S. Szczuka  & 

  3. Institute of Computer Science, Polish Academy of Sciences, 01–237, Warsaw, Poland

    Jerzy W. Grzymała-Busse

  4. Multimedia Systems Department, Gdańsk University of Technology, Narutowicza 11/12, 80-952, Gdańsk, Poland

    Bożena Kostek

  5. Department of Mathematical and Computer Sciences, San Diego State University, 5500 Campanile Drive, CA 92182, San Diego, USA

    Roman W. Świniarski

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© 2004 Springer-Verlag Berlin Heidelberg

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Milton, R.S., Maheswari, V.U., Siromoney, A. (2004). Rough Sets and Relational Learning. In: Peters, J.F., Skowron, A., Grzymała-Busse, J.W., Kostek, B., Świniarski, R.W., Szczuka, M.S. (eds) Transactions on Rough Sets I. Lecture Notes in Computer Science, vol 3100. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27794-1_15

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  • DOI: https://doi.org/10.1007/978-3-540-27794-1_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22374-0

  • Online ISBN: 978-3-540-27794-1

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