A Novel Similarity Measure of Single-Valued Neutrosophic Sets Based on Modified Manhattan Distance and Its Applications

Zeng, Yanqiu; Ren, Haiping; Yang, Tonghua; Xiao, Shixiao; Xiong, Naixue

doi:10.3390/electronics11060941

Cited by 9 publications

(3 citation statements)

References 47 publications

(51 reference statements)

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“…But they did not consider Definition 2.6, and we found an anomaly when we verified their experimental results by the definition of ranking comparison. S RXZ and S ZRY XX proposed by Ren et al [25] and Zeng et al [45] were not fully verified by numerical examples, whereas S RXZ was verified by numerical examples in paper [4] but does not match the examples given in this paper for the Definition 2.6. A detailed analysis of the above problems can be found in Application 1.…”

contrasting

confidence: 63%

Rescue factor for similarity measures of single-valued neutrosophic sets with insufficiencies

Ji,

Chen,

Zhou

et al. 2024

MFC

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The similarity measure is an important indicator of the degree of similarity between two objects and is widely used in pattern recognition, medical diagnosis, and decision-making problems, etc. However, existing similarity measures based on single-valued neutrosophic sets (SVNS) have the weaknesses of insufficient applicability and interpretation, and fail to provide high credibility results. To address this problem, according to conventional ideas, new or improved versions of similarity measures should be constructed to demonstrate their superiority over existing methods. However, it has been increasingly challenging to implement this idea. In this paper, we provide a new idea by adding RFISM, a rescue factor, which can rescue most (some will be filtered out) of the similarity measures that have been shown to be insufficient in previous studies, while preserving the original methods. We implement the new idea by proposing SMS, a set of methods incorporating RFISM. We have compared and analyzed the methods in the SMS with the original methods in detail, and the results show that the selected original methods have all been rescued, with improved applicability and interpretation. Moreover, the new method provides high-confidence results in a specific instance of multi-period medical diagnosis.

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contrasting

confidence: 63%

Rescue factor for similarity measures of single-valued neutrosophic sets with insufficiencies

Ji,

Chen,

Zhou

et al. 2024

MFC

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“…KNN assigns unclassified samples to a class based on their proximity to data points that have been classified before [21]- [22]. This study utilizes Euclidean and Manhattan to calculate the distance between classes, employing the following (3) and ( 4) [23]- [28].…”

Section: Combination Pso Knnmentioning

confidence: 99%

Optimization of Hyperparameter K in K-Nearest Neighbor Using Particle Swarm Optimization

Rizki,

Hermawan,

Avianto

2024

JUITA

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This study aims to enhance the performance of the K-Nearest Neighbors (KNN) algorithm by optimizing the hyperparameter K using the Particle Swarm Optimization (PSO) algorithm. In contrast to prior research, which typically focuses on a single dataset, this study seeks to demonstrate that PSO can effectively optimize KNN hyperparameters across diverse datasets. Three datasets from different domains are utilized: Iris, Wine, and Breast Cancer, each featuring distinct classification types and classes. Furthermore, this research endeavors to establish that PSO can operate optimally with both Manhattan and Euclidean distance metrics. Prior to optimization, experiments with default K values (3, 5, and 7) were conducted to observe KNN behavior on each dataset. Initial results reveal stable accuracy in the iris dataset, while the wine and breast cancer datasets exhibit a decrease in accuracy at K=3, attributed to attribute complexity. The hyperparameter K optimization process with PSO yields a significant increase in accuracy, particularly in the wine dataset, where accuracy improves by 6.28% with the Manhattan matrix. The enhanced accuracy in the optimized KNN algorithm demonstrates the effectiveness of PSO in overcoming KNN constraints. Although the accuracy increase for the iris dataset is not as pronounced, this research provides insight that optimizing the hyperparameter K can yield positive results, even for datasets with initially good performance. A recommendation for future research is to conduct similar experiments with different algorithms, such as Support Vector Machine or Random Forest, to further evaluate PSO's ability to optimize the iris, wine, and breast cancer datasets.

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“…Yang [30] defined a new inclusion relationship of INSs, and gave the new similarity and entropy for the new inclusion relationship. In accordance with the fact that most cases of similarity among SVNSs are often counter-intuitive, Zeng [31] constructed a new distance measure of SVNSs based on the modified Manhattan distance and proposed a new distance-based similarity measure. Ali [32] developed two forms of Hausdorff distance between SVNSs based on the definition of an Hausdorff metric between two sets, and used these new distance measures to construct several similarity measures for SVNSs.…”

Section: Introductionmentioning

confidence: 99%

Similarity Measure for Interval Neutrosophic Sets and Its Decision Application in Resource Offloading of Edge Computing

et al. 2023

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Interval neutrosophic sets (INSs), characterized by truth, indeterminacy and falsity membership degrees, handle the uncertain and inconsistent information that commonly exists in real-life systems, and constitute an extension of the interval valued fuzzy set and interval valued intuitionistic fuzzy set. The existing works on similarity measures for INSs are mostly constructed by distance measures and entropies. Meanwhile, the degree of similarity is expressed as a single number, even if the interval-valued information is considered. This may lead to a loss of interval-valued information. In order to cope with these issues, in this paper, we introduce a new approach to constructing the similarity measures for INSs using fuzzy equivalencies. First, based on fuzzy equivalencies and aggregation operators, the definition of interval-valued fuzzy equivalence is generalized to interval neutrosophic values. Then, based on the framework of INSs, we propose the definition and construction method of the similarity measure using the interval neutrosophic fuzzy equivalence. The similarity degree is expressed as an interval and could retain more information than ever before. In addition, according to practical situations, one can obtain different similarities by selecting the parameters in fuzzy equivalence. Due to the increase in edge computing, it is necessary to reasonably offload the client’s resource and assign them to the edge server to balance the resource usage. The Similarity measure is conductive to select and match the client and edge server. Finally, an illustrative example verifies that the proposed method can find a reasonable client and edge server, as well as effectiveness in the edge computing application.

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A Novel Similarity Measure of Single-Valued Neutrosophic Sets Based on Modified Manhattan Distance and Its Applications

Cited by 9 publications

References 47 publications

Rescue factor for similarity measures of single-valued neutrosophic sets with insufficiencies

Rescue factor for similarity measures of single-valued neutrosophic sets with insufficiencies

Optimization of Hyperparameter K in K-Nearest Neighbor Using Particle Swarm Optimization

Similarity Measure for Interval Neutrosophic Sets and Its Decision Application in Resource Offloading of Edge Computing

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