### 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 language | English |
---|---|

Pages (from-to) | 6441-6453 |

Number of pages | 13 |

Journal | Soft Computing |

Volume | 23 |

Issue number | 15 |

DOIs | |

Publication status | Published - 01-08-2019 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Software
- Theoretical Computer Science
- Geometry and Topology

### Cite this

*Soft Computing*,

*23*(15), 6441-6453. https://doi.org/10.1007/s00500-018-3298-3

}

*Soft Computing*, vol. 23, no. 15, pp. 6441-6453. https://doi.org/10.1007/s00500-018-3298-3

**Markov chains and rough sets.** / Koppula, Kavitha; Kedukodi, Babushri Srinivas; Kuncham, Syam Prasad.

Research output: Contribution to journal › Article

TY - JOUR

T1 - Markov chains and rough sets

AU - Koppula, Kavitha

AU - Kedukodi, Babushri Srinivas

AU - Kuncham, Syam Prasad

PY - 2019/8/1

Y1 - 2019/8/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85048364645&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85048364645&partnerID=8YFLogxK

U2 - 10.1007/s00500-018-3298-3

DO - 10.1007/s00500-018-3298-3

M3 - Article

VL - 23

SP - 6441

EP - 6453

JO - Soft Computing

JF - Soft Computing

SN - 1432-7643

IS - 15

ER -