Automatic determination about precision parameter value based on inclusion degree with variable precision rough set model

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.

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“…Considering an improvement of Cheng et al (2015), we define relative discernibility of decision tables as follows.…”

Section: Evidence Theory and Vp-models In Decision Tablesmentioning

confidence: 99%

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

2016

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“…Wang and Zhou [6] define interval reduction based on

β

‐positive region, but some kinds of reduction anomalies still exist. To avoid all reduction anomalies, we define interval reduction based on β ‐lower approximation distribution [7–9]. We analyse the property of interval core attribute that is the most important attribute and put forward a method to get the interval core attribute on β ‐lower approximation distribution hierarchy [10–13].…”

Section: Introductionmentioning

confidence: 99%

Research of interval reduction in variable precision rough set

Chen

Jiang

2020

J. eng.

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“…For example, Cheng et al [3] computed precision parameter values based on inclusion degree with variable precision rough set model. Deng and Yao [4,5] presented an information-theoretic approach to the interpreation and determination of thresholds used in PRS and presented DTRS-based three-way approximations of fuzzy sets.…”

Section: Introductionmentioning

confidence: 99%

Decision-theoretic rough sets based on time-dependent loss function

Lang

2015

Preprint

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A fundamental notion of decision-theoretic rough sets is the concept of loss functions, which provides a powerful tool of calculating a pair of thresholds for making a decision with a minimum cost. In this paper, time-dependent loss functions which are variations of the time are of interest because such functions are frequently encountered in practical situations, we present the relationship between the pair of thresholds and loss functions satisfying time-dependent uniform distributions and normal processes in light of bayesian decision procedure. Subsequently, with the aid of bayesian decision procedure, we provide the relationship between the pair of thresholds and loss functions which are time-dependent interval sets and fuzzy numbers. Finally, we employ several examples to illustrate that how to calculate the thresholds for making a decision by using time-dependent loss functions-based decision-theoretic rough sets.

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Automatic determination about precision parameter value based on inclusion degree with variable precision rough set model

Cited by 10 publications

References 36 publications

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

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

Research of interval reduction in variable precision rough set

Decision-theoretic rough sets based on time-dependent loss function

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