Soft sets and soft rough sets

Feng, Feng; Liu, Xiaoyan; Leoreanu-Fotea, V.; Jun, Young Bae

doi:10.1016/j.ins.2010.11.004

Cited by 487 publications

(273 citation statements)

References 35 publications

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“…It can be seen from figure 2 that, in the early stage of dynamic adjustment coefficient, the optimal decision object is 4 x . With the constant change of  , the optimal decision object is changed from 4 x to 5 x . When 0.5   , the optimal decision object is 4 x and then 5 x again.…”

Section: The Example Analysismentioning

confidence: 99%

“…When 0.5   , the optimal decision object is 4 x and then 5 x again. When 0.9   , the optimal decision object is changed from 5 x to 2 x .This is because the combination weights for the linear function of  , each property parameters of monotonicity is different, the  value is not at the same time, combination weights can affect the decision result, resulting in different optimal decision. On the whole, before 0.5   , the subjective weight has a great influence on the optimal decision object, and the optimal decision object in the early stage is 4 x .…”

Section: The Example Analysismentioning

confidence: 99%

“…Based on the study of soft set theory, many scholars put forward some new theories. Feng [5] and others put forward the concept of rough set, making soft set theory applied to rough set.Maji [6] and others combine fuzzy sets with soft sets theory to give the concept of fuzzy soft sets. Now fuzzy set has been widely used in various fields, such as economics, medicine and environmental science.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fuzzy Soft Set Multi-Attribute Decision Making Method Based on TOPSIS with Improved Entropy Weight

Liu¹,

Liu²

2018

Proceedings of the 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)

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Abstract. In the process of fuzzy soft set multi-attribute decision making, and It is difficult to determine the weight of the attribute parameters which results in low efficiency of decision making. Therefore, a new method of determining the entropy weight which is calculated by the notion of the importance degree and the deviation degree based on parameters, moreover entropy weight is used to determine the objective weight of attribute parameters. The dynamic weight determination method is proposed to determine the combined weight which combines the subjective weight and the objective weight, then combined with the traditional TOPSIS method, this paper proposes the method of fuzzy soft set multi-attribute decision making based on TOPSIS with improved entropy weight. Finally, a practical example has been analyzed to verify the feasibility and effectiveness of the approach, which can realize the dynamic decision making according to the demand of decision making and improve the efficiency of decision making.

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Section: The Example Analysismentioning

confidence: 99%

Section: The Example Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fuzzy Soft Set Multi-Attribute Decision Making Method Based on TOPSIS with Improved Entropy Weight

Liu¹,

Liu²

2018

Proceedings of the 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)

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show abstract

“…Recently, Molodtsov [11] introduced the soft set as a mathematical tool for dealing information as the uncertainty of data in engineering, physics, computer sciences and many other diverse field. Presently, the soft set theory is making progress rapidly [1,4]. Pawlak's rough set [12,13] can be viewed as a special case of soft rough sets [4].…”

Section: Introductionmentioning

confidence: 99%

“…Presently, the soft set theory is making progress rapidly [1,4]. Pawlak's rough set [12,13] can be viewed as a special case of soft rough sets [4]. The topological structures of soft sets have been developed by many researchers [2,[7][8][9][15][16][17].…”

Section: Introductionmentioning

confidence: 99%