CEQD: A Complex Mass Function to Predict Interference Effects

Xiao, Fuyuan

doi:10.1109/tcyb.2020.3040770

Cited by 143 publications

(54 citation statements)

References 86 publications

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“…Some distance-based methodology widely used for pattern recognition tasks, could be potentially tailored to provide indications and establish priorities for vaccine administration. These methods may include the complex evidential distance [20] , the complex evidential quantum dynamical model [21] and a complex mass function [22] that are proposed to measure the difference or dissimilarity among pieces of evidence. In particular, the totality of these approaches were embedded in the decision-making algorithm for the purpose of solving a pattern recognition task in a medical diagnosis scenario.…”

Section: Related Workmentioning

confidence: 99%

A Unified Hierarchical XGBoost model for classifying priorities for COVID-19 vaccination campaign

Romeo

Frontoni

2022

Pattern Recognition

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The current ML approaches do not fully focus to answer a still unresolved and topical challenge, namely the prediction of priorities of COVID-19 vaccine administration. Thus, our task includes some additional methodological challenges mainly related to avoiding unwanted bias while handling categorical and ordinal data with a highly imbalanced nature. Hence, the main contribution of this study is to propose a machine learning algorithm, namely Hierarchical Priority Classification eXtreme Gradient Boosting for priority classification for COVID-19 vaccine administration using the Italian Federation of General Practitioners dataset that contains Electronic Health Record data of 17k patients. We measured the effectiveness of the proposed methodology for classifying all the priority classes while demonstrating a significant improvement with respect to the state of the art. The proposed ML approach, which is integrated into a clinical decision support system, is currently supporting General Pracitioners in assigning COVID-19 vaccine administration priorities to their assistants.

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Section: Related Workmentioning

confidence: 99%

A Unified Hierarchical XGBoost model for classifying priorities for COVID-19 vaccination campaign

Romeo

Frontoni

2022

Pattern Recognition

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“…Zhou et al [49] applied the complex cubic fuzzy aggregation operators in group decision making. Xiao [50][51][52] studied the complex mass functions and used them to predict the interference effects as well as discussed the distance for complex mass functions.…”

Section: Figure 2 Evaluation Of Ranges Of Pfs Sfs and Tsfsmentioning

confidence: 99%

Complex T-Spherical Fuzzy Relations With Their Applications in Economic Relationships and International Trades

et al. 2021

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Uncertainty is the unavoidable part of the life. In almost all circumstances, we regularly find ourselves in a state of uncertainty. Several reasons can lead to uncertainty, such as randomness, vagueness and rough knowledge. Fuzzy set (FS) theory deals with these kinds of information. Many generalizations had been made in the theory of FSs, such as intuitionistic FSs (IFSs), q-rung orthopair FSs (qROFSs), complex qROFSs (CqROFSs), spherical FSs (SFSs), T-spherical FSs (TSFSs), and complex TSFSs (CTSFSs). Among these generalizations of FSs, the CTSFSs are the most dominant generalization of the FSs. Although fuzzy relations (FRs), IF relations (IFRs) and complex FRs (CFRs) were defined in the literature, the concepts of relations have not yet been introduced in the CTSFSs. This paper unveils the novel concept of CTSF relations (CTSFRs), which provides the extensive generalizations of FRs. The proposed CTSFRs can give many generalized types of FRs, such as IFRs, CFRs, Pythagorean FRs, qROFRs, SFRs and TSFRs, etc. Additionally, some useful properties and results are obtained for CTSFRs. Moreover, a couple of applications demonstrate the usefulness of the proposed concepts. These CTSFRs can be used to depict the time-related interdependence of global market. Thus, we apply these CTSFRs to analyze the interdependence of the international trades among countries and compare the financial factors affecting business markets. Furthermore, the economic relationships with respect to time lag can be modeled by using the CTSFSs and the CTSFRs. Finally, a comparative analysis illuminates the supremacy of the proposed way in contrast with the existing ones.INDEX TERMS Fuzzy sets (FSs); T-spherical FSs (TSFSs); Complex TSFSs (CTSFSs); CTSF relations (CTSFRs); Cartesian product in CTSFSs; CTSF composite relation; Financial and economic relationships.

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“…The decision process can be given as in the previous example. For each pair (x c , i j ), the combined BPAs can be calculated as in Equation (9). So the weighted evaluation values of alternative x i can be expressed by the structure…”

Section: The Limitations Of Applying Dst To the Operation Of Ifvsmentioning

confidence: 99%

“…[1][2][3] In the process of MCDM, the evaluation information derived from different experts or other sources is usually imperfect, that is, ambiguous, uncertain, and even conflicting. 4,5 To express the information more effectively, quite a few fuzziness theories, such as fuzzy sets (FSs), [6][7][8] intuitionistic fuzzy sets (IFSs), [9][10][11][12] Pythagorean fuzzy sets (PFSs), 13,14 and hesitant fuzzy sets (HFSs), [15][16][17] have been developed. Among these fuzzy theories, IFS has been widely employed in MCDM problems due to its flexibility in representing and managing uncertain information.…”

Section: Introductionmentioning

confidence: 99%

Intuitionistic fuzzy decision‐making in the framework of Dempster–Shafer structures

Fei

Feng

2021

Int J Intell Syst

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CEQD: A Complex Mass Function to Predict Interference Effects

Cited by 143 publications

References 86 publications

A Unified Hierarchical XGBoost model for classifying priorities for COVID-19 vaccination campaign

A Unified Hierarchical XGBoost model for classifying priorities for COVID-19 vaccination campaign

Complex T-Spherical Fuzzy Relations With Their Applications in Economic Relationships and International Trades

Intuitionistic fuzzy decision‐making in the framework of Dempster–Shafer structures

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