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Synthesis of Decision Systems from Data Tables

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Rough Sets and Data Mining

Abstract

We discuss two basic questions related to the synthesis of decision algorithms.

The first question can be formulated as follows: what strategies can be used in order to discover the decision rules from experimental data? Answering this question, we propose to build these strategies on the basis of rough set methods and Boolean reasoning techniques. We present some applications of these methods for extracting decision rules from decision tables used to represent experimental data.

The second question can be formulated as follows: what is a general framework for approximate reasoning in distributed systems? Answering this question, we assume that distributed systems are organized on rough mereological principles in order to assembly (construct) complex objects satisfying a given specification in a satisfactory degree. We discuss how this approach can be used for building the foundations for approximate reasoning. Our approach is based on rough mereology, the recently developed extension of mereology of Leśniewski.

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Authors and Affiliations

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

    Andrzej Skowron

  2. Institute of Mathematics, Warsaw University of Technology, Pl. Politechniki 1, 00-650, Warsaw, Poland

    Lech Polkowski

Authors
  1. Andrzej Skowron
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  2. Lech Polkowski
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© 1997 Kluwer Academic Publishers

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Skowron, A., Polkowski, L. (1997). Synthesis of Decision Systems from Data Tables. In: Rough Sets and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-1461-5_14

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  • DOI: https://doi.org/10.1007/978-1-4613-1461-5_14

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4612-8637-0

  • Online ISBN: 978-1-4613-1461-5

  • eBook Packages: Springer Book Archive

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