Attribute Reduction Methods Based on Pythagorean Fuzzy Covering Information Systems

Chen, Yan; Zhang, Haidong

doi:10.1109/access.2020.2972343

Cited by 2 publications

(5 citation statements)

References 45 publications

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“…(2) Our attribute reduction algorithm is simpler, more effective, and more accurate than the attribute reduction algorithm proposed in [18], which is shown in Section 5.2.…”

Section: Rulesmentioning

confidence: 97%

“…Using the attribute reduction Algorithm 1, we can show the results of our proposed model and compare it with the methods from the literature [18]. Table 3 illustrates how our model is superior and rational.…”

Section: Superiority and Rationality Analysismentioning

confidence: 99%

“…Table 3: The decision rules based on probabilities with respect to {a 2 , a 4 } by method proposed in [18].…”

Section: Superiority and Rationality Analysismentioning

confidence: 99%

“…Table 3 shows the number and quality of objects that are correctly classified by the methods from the literature [18]. Their quality values are 59.4% and 72.8%, respectively.…”

Section: Rulesmentioning

confidence: 99%

“…In [21], Zhang and Ma studied PFDTRS based on Pythagorean fuzzy covering. A new rough set model that uses PFSs to handle uncertainty in covering information systems studied in [18]. The study also presents a method to find attribute reduction based on the discernibility matrix of the rough set model.…”

Section: Introductionmentioning

confidence: 99%

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Conflict distance-based variable precision Pythagorean fuzzy rough set in Pythagorean fuzzy decision systems with applications in decision making

Sahoo,

Guchhait,

Allahviranloo

et al. 2024

J. Math. Computer Sci.

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Real-life decision-making problems are hard to handle by any single uncertainty method because of the complex and uncertain nature of the physical world and the human limitations in understanding it. Therefore, we naturally consider combining the benefits of various uncertainty theories to create a more effective hybrid soft decision-making method. Based on this idea, we use the variable precision rough sets (VPRSs) and Pythagorean fuzzy sets approach to build a new Pythagorean fuzzy rough set (PFRS) model. Since the information system is Pythagorean fuzzy, we use the Pythagorean fuzzy similarity measure to define the new type of distance based on conflict. Then, we merge this notion with the VPRSs to form a variable precision PFRS model and study its properties. We also propose an algorithm for attribute reduction based on this model and apply it to a case study to test its feasibility and performance. The results demonstrate that our model enhances the classification capability of previous models, and it achieves accurate classification by deriving the decision rules.

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“…(2) Our attribute reduction algorithm is simpler, more effective, and more accurate than the attribute reduction algorithm proposed in [18], which is shown in Section 5.2.…”

Section: Rulesmentioning

confidence: 97%

Section: Superiority and Rationality Analysismentioning

confidence: 99%

“…Table 3: The decision rules based on probabilities with respect to {a 2 , a 4 } by method proposed in [18].…”

Section: Superiority and Rationality Analysismentioning

confidence: 99%

“…Table 3 shows the number and quality of objects that are correctly classified by the methods from the literature [18]. Their quality values are 59.4% and 72.8%, respectively.…”

Section: Rulesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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