Markov chains and rough sets

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

In this paper, we present a link between markov chains and rough sets. A rough approximation framework (RAF) gives a set of approximations for a subset of universe. Rough approximations using a collection of reference points gives rise to a RAF. We use the concept of markov chains and introduce the notion of a Markov rough approximation framework (MRAF), wherein a probability distribution function is obtained corresponding to a set of rough approximations. MRAF supplements well-known multi-attribute decision-making methods like TOPSIS and VIKOR in choosing initial weights for the decision criteria. Further, MRAF creates a natural route for deeper analysis of data which is very useful when the values of the ranked alternatives are close to each other. We give an extension to Pawlak’s decision algorithm and illustrate the idea of MRAF with explicit example from telecommunication networks.

Original languageEnglish
Pages (from-to)6441-6453
Number of pages13
JournalSoft Computing
Volume23
Issue number15
DOIs
Publication statusPublished - 01-08-2019

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Rough Set
Markov processes
Markov chain
Rough
Approximation
Probability distributions
Telecommunication networks
Distribution functions
Decision making
Multi-attribute Decision Making
TOPSIS
Telecommunication Network
Reference Point
Probability Distribution Function
Framework
Subset
Alternatives

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Geometry and Topology

Cite this

Koppula, Kavitha ; Kedukodi, Babushri Srinivas ; Kuncham, Syam Prasad. / Markov chains and rough sets. In: Soft Computing. 2019 ; Vol. 23, No. 15. pp. 6441-6453.
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Markov chains and rough sets. / Koppula, Kavitha; Kedukodi, Babushri Srinivas; Kuncham, Syam Prasad.

In: Soft Computing, Vol. 23, No. 15, 01.08.2019, p. 6441-6453.

Research output: Contribution to journalArticle

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