Inclusion degree with variable-precision model in analyzing inconsistent decision tables

Yu Ru Syau; Andrzej Skowron; En Bing Lin

doi:10.1007/s41066-016-0027-0

Inclusion degree with variable-precision model in analyzing inconsistent decision tables

Yu Ru Syau, Andrzej Skowron, En Bing Lin

Mathematics

Research output: Contribution to journal › Article › peer-review

13 Scopus citations

Abstract

In this paper, we incorporate the concepts of evidence theory and variable precision rough set model (VP-model) to examine inconsistent decision tables. For the decision classes in a given decision table, we point out the relationship between approximation degree of dependency and the belief functions. We also introduce the notion of weakly discernable decision tables, and show how to obtain the discernibility threshold of a given weakly discernable decision table. We prove that weakly discernibility is a necessary condition of relative consistency in a given decision table.

Original language	English
Pages (from-to)	65-72
Number of pages	8
Journal	Granular Computing
Volume	2
Issue number	2
DOIs	https://doi.org/10.1007/s41066-016-0027-0
State	Published - Jun 2017

Keywords

Belief functions
Inclusion degrees
Inconsistent decision tables
Relative discernibility
Rough sets
Variable precision rough set models

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10.1007/s41066-016-0027-0

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title = "Inclusion degree with variable-precision model in analyzing inconsistent decision tables",

abstract = "In this paper, we incorporate the concepts of evidence theory and variable precision rough set model (VP-model) to examine inconsistent decision tables. For the decision classes in a given decision table, we point out the relationship between approximation degree of dependency and the belief functions. We also introduce the notion of weakly discernable decision tables, and show how to obtain the discernibility threshold of a given weakly discernable decision table. We prove that weakly discernibility is a necessary condition of relative consistency in a given decision table.",

keywords = "Belief functions, Inclusion degrees, Inconsistent decision tables, Relative discernibility, Rough sets, Variable precision rough set models",

author = "Syau, {Yu Ru} and Andrzej Skowron and Lin, {En Bing}",

note = "Funding Information: The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper. Publisher Copyright: {\textcopyright} 2016, Springer International Publishing Switzerland.",

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AU - Syau, Yu Ru

AU - Skowron, Andrzej

AU - Lin, En Bing

N1 - Funding Information: The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper. Publisher Copyright: © 2016, Springer International Publishing Switzerland.

PY - 2017/6

Y1 - 2017/6

N2 - In this paper, we incorporate the concepts of evidence theory and variable precision rough set model (VP-model) to examine inconsistent decision tables. For the decision classes in a given decision table, we point out the relationship between approximation degree of dependency and the belief functions. We also introduce the notion of weakly discernable decision tables, and show how to obtain the discernibility threshold of a given weakly discernable decision table. We prove that weakly discernibility is a necessary condition of relative consistency in a given decision table.

AB - In this paper, we incorporate the concepts of evidence theory and variable precision rough set model (VP-model) to examine inconsistent decision tables. For the decision classes in a given decision table, we point out the relationship between approximation degree of dependency and the belief functions. We also introduce the notion of weakly discernable decision tables, and show how to obtain the discernibility threshold of a given weakly discernable decision table. We prove that weakly discernibility is a necessary condition of relative consistency in a given decision table.

KW - Belief functions

KW - Inclusion degrees

KW - Inconsistent decision tables

KW - Relative discernibility

KW - Rough sets

KW - Variable precision rough set models

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VL - 2

SP - 65

EP - 72

JO - Granular Computing

JF - Granular Computing

IS - 2

ER -

Inclusion degree with variable-precision model in analyzing inconsistent decision tables

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Keywords

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