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Rough Sets and Vague Concept Approximation: From Sample Approximation to Adaptive Learning

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Transactions on Rough Sets V

Part of the book series: Lecture Notes in Computer Science ((TRS,volume 4100))

Abstract

We present a rough set approach to vague concept approximation. Approximation spaces used for concept approximation have been initially defined on samples of objects (decision tables) representing partial information about concepts. Such approximation spaces defined on samples are next inductively extended on the whole object universe. This makes it possible to define the concept approximation on extensions of samples. We discuss the role of inductive extensions of approximation spaces in searching for concept approximation. However, searching for relevant inductive extensions of approximation spaces defined on samples is infeasible for compound concepts. We outline an approach making this searching feasible by using a concept ontology specified by domain knowledge and its approximation. We also extend this approach to a framework for adaptive approximation of vague concepts by agents interacting with environments. This paper realizes a step toward approximate reasoning in multiagent systems (MAS), intelligent systems, and complex dynamic systems (CAS).

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Author information

Authors and Affiliations

  1. Institute of Mathematics, University of Rzeszów, Rejtana 16A, 35-959, Rzeszów, Poland

    Jan Bazan

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

    Andrzej Skowron

  3. Institute of Computer Science, Polish Academy of Sciences, Ordona 21, 01-237, Warsaw, Poland

    Roman Swiniarski

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

    Roman Swiniarski

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  1. Jan Bazan
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  2. Andrzej Skowron
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  3. Roman Swiniarski
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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

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Bazan, J., Skowron, A., Swiniarski, R. (2006). Rough Sets and Vague Concept Approximation: From Sample Approximation to Adaptive Learning. In: Peters, J.F., Skowron, A. (eds) Transactions on Rough Sets V. Lecture Notes in Computer Science, vol 4100. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11847465_3

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  • DOI: https://doi.org/10.1007/11847465_3

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