Three-Way Decisions Making Using Covering Based Fractional Orthotriple Fuzzy Rough Set Model

Abosuliman, Shougi Suliman; Abdullah, Saleem; Qiyas, Muhammad

doi:10.3390/math8071121

Cited by 27 publications

(16 citation statements)

References 73 publications

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“…• we put f = 1 and η = υ = 0 in definition (5), then FOFS reduces to FS This remark identifies that SFS, PyFS, PFS, IFS, and FS are the special cases of FOFS [44].…”

Section: Remark 1 We Know That Ifmentioning

confidence: 92%

“…Recently, Wei et al [42] proposed the SMs of SFS using the cosine function. Shougi et al [44] defined a three-way DM using covering based FOF Rough Set Model.…”

Section: Literature Reviewmentioning

confidence: 99%

“…the refusal grade of ∈ X in . For convenience, (µ , η , ν ) is said to be a fractional orthotriple fuzzy number (FOFNs) [44].…”

Section: Definition 5 a Fofs On X Is Of The Shapementioning

confidence: 99%

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Similarity Measures for Fractional Orthotriple Fuzzy Sets Using Cosine and Cotangent Functions and Their Application in Accident Emergency Response

et al. 2020

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The fractional orthotriple fuzzy set (FOFS) is more generalized than the spherical fuzzy set (SFS) and picture fuzzy set (PFS) to cope with awkward and complex information in fuzzy set (FS) theory. The FOFS is a more powerful technique with respect to the existing drawbacks because of its conditions, i.e., the sum of the f powers of positive, neutral, and negative grades is bounded to [0,1]. With the advantages of the FOFS, in this paper, we study the basic definitions and some existing similarity measures (SMs) of intuitionistic fuzzy sets (IFSs), PFSs, Pythagorean fuzzy sets (PyFSs) and SFSs. The existing approaches have certain limitations and cannot be applied to problems that are in the form of FOFSs. The goal of this paper is to propose the idea of some new SMs including cosine SMs for FOFSs, SMs for FOFSs based on the cosine function, and SMs for FOFSs based on the cotangent function. Further, some weighted SMs (WSMs) are also proposed for which the weight of the attributes is considered. Then, we apply these SMs and WSMs to the pattern recognition problem. Finally, the comparative study of the new SMs for FOFSs is established with existing SMs, and also, some advantages of the proposed work are discussed.

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“…• we put f = 1 and η = υ = 0 in definition (5), then FOFS reduces to FS This remark identifies that SFS, PyFS, PFS, IFS, and FS are the special cases of FOFS [44].…”

Section: Remark 1 We Know That Ifmentioning

confidence: 92%

“…Recently, Wei et al [42] proposed the SMs of SFS using the cosine function. Shougi et al [44] defined a three-way DM using covering based FOF Rough Set Model.…”

Section: Literature Reviewmentioning

confidence: 99%

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Similarity Measures for Fractional Orthotriple Fuzzy Sets Using Cosine and Cotangent Functions and Their Application in Accident Emergency Response

et al. 2020

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“…fuzzy rough sets applies fuzzy equivalence classes, making use of fuzzy indiscernibility [40,41]. It allows for expressing imprecise knowledge about similarity and dissimilarity between objects by fuzzy membership functions.…”

Section: Other Rough Set Approachesmentioning

confidence: 99%

Mining Knowledge of Respiratory Rate Quantification and Abnormal Pattern Prediction

et al. 2021

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The described application of granular computing is motivated because cardiovascular disease (CVD) remains a major killer globally. There is increasing evidence that abnormal respiratory patterns might contribute to the development and progression of CVD. Consequently, a method that would support a physician in respiratory pattern evaluation should be developed. Group decision-making, tri-way reasoning, and rough set–based analysis were applied to granular computing. Signal attributes and anthropomorphic parameters were explored to develop prediction models to determine the percentage contribution of periodic-like, intermediate, and normal breathing patterns in the analyzed signals. The proposed methodology was validated employing k-nearest neighbor (k-NN) and UMAP (uniform manifold approximation and projection). The presented approach applied to respiratory pattern evaluation shows that median accuracies in a considerable number of cases exceeded 0.75. Overall, parameters related to signal analysis are indicated as more important than anthropomorphic features. It was also found that obesity characterized by a high WHR (waist-to-hip ratio) and male sex were predisposing factors for the occurrence of periodic-like or intermediate patterns of respiration. It may be among the essential findings derived from this study. Based on classification measures, it may be observed that a physician may use such a methodology as a respiratory pattern evaluation-aided method.

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“…We assume that one attribute a belongs to the B set, where B ⊆ A . For the B set, if I ( B ) = I ( B − { a }), then attribute a is unnecessary; however, for a specific case in which the above formula is unfulfilled, attribute a is necessary from the B set [ 42 ]. The unnecessary attributes are removed from the subsets, and these are then called the reduced sets [ 43 ].…”

Section: Related Workmentioning

confidence: 99%

Identification of the Framingham Risk Score by an Entropy-Based Rule Model for Cardiovascular Disease

Chen

Cheng

Chen

et al. 2020

Entropy

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Since 2001, cardiovascular disease (CVD) has had the second-highest mortality rate, about 15,700 people per year, in Taiwan. It has thus imposed a substantial burden on medical resources. This study was triggered by the following three factors. First, the CVD problem reflects an urgent issue. A high priority has been placed on long-term therapy and prevention to reduce the wastage of medical resources, particularly in developed countries. Second, from the perspective of preventive medicine, popular data-mining methods have been well learned and studied, with excellent performance in medical fields. Thus, identification of the risk factors of CVD using these popular techniques is a prime concern. Third, the Framingham risk score is a core indicator that can be used to establish an effective prediction model to accurately diagnose CVD. Thus, this study proposes an integrated predictive model to organize five notable classifiers: the rough set (RS), decision tree (DT), random forest (RF), multilayer perceptron (MLP), and support vector machine (SVM), with a novel use of the Framingham risk score for attribute selection (i.e., F-attributes first identified in this study) to determine the key features for identifying CVD. Verification experiments were conducted with three evaluation criteria—accuracy, sensitivity, and specificity—based on 1190 instances of a CVD dataset available from a Taiwan teaching hospital and 2019 examples from a public Framingham dataset. Given the empirical results, the SVM showed the best performance in terms of accuracy (99.67%), sensitivity (99.93%), and specificity (99.71%) in all F-attributes in the CVD dataset compared to the other listed classifiers. The RS showed the highest performance in terms of accuracy (85.11%), sensitivity (86.06%), and specificity (85.19%) in most of the F-attributes in the Framingham dataset. The above study results support novel evidence that no classifier or model is suitable for all practical datasets of medical applications. Thus, identifying an appropriate classifier to address specific medical data is important. Significantly, this study is novel in its calculation and identification of the use of key Framingham risk attributes integrated with the DT technique to produce entropy-based decision rules of knowledge sets, which has not been undertaken in previous research. This study conclusively yielded meaningful entropy-based knowledgeable rules in tree structures and contributed to the differentiation of classifiers from the two datasets with three useful research findings and three helpful management implications for subsequent medical research. In particular, these rules provide reasonable solutions to simplify processes of preventive medicine by standardizing the formats and codes used in medical data to address CVD problems. The specificity of these rules is thus significant compared to those of past research.

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Three-Way Decisions Making Using Covering Based Fractional Orthotriple Fuzzy Rough Set Model

Cited by 27 publications

References 73 publications

Similarity Measures for Fractional Orthotriple Fuzzy Sets Using Cosine and Cotangent Functions and Their Application in Accident Emergency Response

Similarity Measures for Fractional Orthotriple Fuzzy Sets Using Cosine and Cotangent Functions and Their Application in Accident Emergency Response

Mining Knowledge of Respiratory Rate Quantification and Abnormal Pattern Prediction

Identification of the Framingham Risk Score by an Entropy-Based Rule Model for Cardiovascular Disease

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