On similarity and entropy of neutrosophic soft sets

Şahin, Rıdvan; Küçük, Ahmet

doi:10.3233/ifs-141211

Cited by 66 publications

(14 citation statements)

References 47 publications

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“…Remark 1 [32]. On account of the single valued neutrosophic set is an instance of the neutrosophic set, it is natural to infer that a single valued neutrosophic soft set is an instance of the neutrosophic soft set.…”

Section: Definition 6 [4] (P 1)mentioning

confidence: 99%

Algorithm for Neutrosophic Soft Sets in Stochastic Multi-Criteria Group Decision Making Based on Prospect Theory

2019

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To address issues involving inconsistencies, this paper proposes a stochastic multi-criteria group decision making algorithm based on neutrosophic soft sets, which includes a pair of asymmetric functions: Truth-membership and false-membership, and an indeterminacy-membership function. For integrating an inherent stochastic, the algorithm expresses the weights of decision makers and parameter subjective weights by neutrosophic numbers instead of determinate values. Additionally, the algorithm is guided by the prospect theory, which incorporates psychological expectations of decision makers into decision making. To construct the prospect decision matrix, this research establishes a conflict degree measure of neutrosophic numbers and improves it to accommodate the stochastic multi-criteria group decision making. Moreover, we introduce the weighted average aggregation rule and weighted geometric aggregation rule of neutrosophic soft sets. Later, this study presents an algorithm for neutrosophic soft sets in the stochastic multi-criteria group decision making based on the prospect theory. Finally, we perform an illustrative example and a comparative analysis to prove the effectiveness and feasibility of the proposed algorithm.

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Section: Definition 6 [4] (P 1)mentioning

confidence: 99%

Algorithm for Neutrosophic Soft Sets in Stochastic Multi-Criteria Group Decision Making Based on Prospect Theory

2019

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“…We present several distance measures between Q-NSSs, specifically the distances of Hamming, Euclidean, normalized Hamming and normalized Euclidean. These distances are obtained through generalizing the corresponding measures of distances for NSSs characterized in [ 27 ], to make them appropriate to the basis of Q-NSSs.…”

Section: Measures Of Distance and Similarity Of Q-neutrosophic Sofmentioning

confidence: 99%

“…Selvachandran et al [ 25 ] discussed the intuitionistic entropy of generalized intuitionistic fuzzy soft set. Majumdar and Samanta [ 26 ] proposed the entropy of neutrosophic sets and then the entropy of NSS was defined [ 27 ]. Recently, many researchers are paying more attention to different information measures and their applications on various hybrid models of fuzzy soft set [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ].…”

Section: Introductionmentioning

confidence: 99%

Entropy, Measures of Distance and Similarity of Q-Neutrosophic Soft Sets and Some Applications

Qamar

Hassan

2018

Entropy

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The idea of the Q-neutrosophic soft set emerges from the neutrosophic soft set by upgrading the membership functions to a two-dimensional entity which indicate uncertainty, indeterminacy and falsity. Hence, it is able to deal with two-dimensional inconsistent, imprecise, and indeterminate information appearing in real life situations. In this study, the tools that measure the similarity, distance and the degree of fuzziness of Q-neutrosophic soft sets are presented. The definitions of distance, similarity and measures of entropy are introduced. Some formulas for Q-neutrosophic soft entropy were presented. The known Hamming, Euclidean and their normalized distances are generalized to make them well matched with the idea of Q-neutrosophic soft set. The distance measure is subsequently used to define the measure of similarity. Lastly, we expound three applications of the measures of Q-neutrosophic soft sets by applying entropy and the similarity measure to a medical diagnosis and decision making problems.

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“…Babitha and Sunil gave the idea of soft set relations [11]. In [12], Sahin and Kucuk presented NSS in the form of neutrosophic relation.…”

Section: Introductionmentioning

confidence: 99%

Multi-Attribute Decision-Making Method Based on Neutrosophic Soft Rough Information

Akram

Shahzadi

Smarandache

2018

Axioms

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Soft sets (SSs), neutrosophic sets (NSs), and rough sets (RSs) are different mathematical models for handling uncertainties, but they are mutually related. In this research paper, we introduce the notions of soft rough neutrosophic sets (SRNSs) and neutrosophic soft rough sets (NSRSs) as hybrid models for soft computing. We describe a mathematical approach to handle decision-making problems in view of NSRSs. We also present an efficient algorithm of our proposed hybrid model to solve decision-making problems.

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On similarity and entropy of neutrosophic soft sets

Cited by 66 publications

References 47 publications

Algorithm for Neutrosophic Soft Sets in Stochastic Multi-Criteria Group Decision Making Based on Prospect Theory

Algorithm for Neutrosophic Soft Sets in Stochastic Multi-Criteria Group Decision Making Based on Prospect Theory

Entropy, Measures of Distance and Similarity of Q-Neutrosophic Soft Sets and Some Applications

Multi-Attribute Decision-Making Method Based on Neutrosophic Soft Rough Information

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