This paper introduces a new aggregation model by using induced and heavy aggregation operators in distances measures such as the Hamming distance.It is called the induced heavy ordered weighted averaging(OWA) dista...This paper introduces a new aggregation model by using induced and heavy aggregation operators in distances measures such as the Hamming distance.It is called the induced heavy ordered weighted averaging(OWA) distance(IHOWAD) operator.This paper studies some of its main properties and a wide range of particular cases such as the induced heavy OWA(IHOWA) operator,the induced OWA distance(IOWAD) operator and the heavy OWA distance(HOWAD) operator.This approach is generalized by using generalized and quasi-arithmetic means obtaining the induced generalized IHOWAD(IGHOWAD) operator and the Quasi-IHOWAD operator.An application of the new approach in a decision making problem regarding the selection of strategies is developed.展开更多
In recent years,decision-making under uncertainty has attracted substantial attention in both academia and industry,with a growing number of organizations prioritizing decision support for talent evaluation.Vague set ...In recent years,decision-making under uncertainty has attracted substantial attention in both academia and industry,with a growing number of organizations prioritizing decision support for talent evaluation.Vague set theory has been recognized as a powerful tool to address the ambiguity of problem parameters and manage uncertainty.This paper introduces a novel talent evaluation method that harnesses the potential of vague sets.We construct a vague set Ordered Weighted Averaging(OWA)operator for offering a robust solution to intricate decision-making problems,especially in talent evaluation.The application of the OWA operator augments the decision-making process by providing a mechanism to handle the aggregation of information in a more flexible and comprehensive manner.Experimental results show the effectiveness of the proposed method,presenting an alternative for decision-makers,aiding them in selecting their preferred choices amidst uncertainty.展开更多
The problem of missing values has long been studied by researchers working in areas of data science and bioinformatics,especially the analysis of gene expression data that facilitates an early detection of cancer.Many...The problem of missing values has long been studied by researchers working in areas of data science and bioinformatics,especially the analysis of gene expression data that facilitates an early detection of cancer.Many attempts show improvements made by excluding samples with missing information from the analysis process,while others have tried to fill the gaps with possible values.While the former is simple,the latter safeguards information loss.For that,a neighbour-based(KNN)approach has proven more effective than other global estimators.The paper extends this further by introducing a new summarizationmethod to theKNNmodel.It is the first study that applies the concept of ordered weighted averaging(OWA)operator to such a problem context.In particular,two variations of OWA aggregation are proposed and evaluated against their baseline and other neighbor-based models.Using different ratios of missing values from 1%-20%and a set of six published gene expression datasets,the experimental results suggest that newmethods usually provide more accurate estimates than those compared methods.Specific to the missing rates of 5%and 20%,the best NRMSE scores as averages across datasets is 0.65 and 0.69,while the highest measures obtained by existing techniques included in this study are 0.80 and 0.84,respectively.展开更多
A generalization of the linguistic aggregation functions (or operators) is presented by using generalized and quasiarithmetic means. Firstly, the linguistic weighted generalized mean (LWGM) and the linguistic gene...A generalization of the linguistic aggregation functions (or operators) is presented by using generalized and quasiarithmetic means. Firstly, the linguistic weighted generalized mean (LWGM) and the linguistic generalized ordered weighted averaging (LGOWA) operator are introduced. These aggregation functions use linguistic information and generalized means in the weighted average (WA) and in the ordered weighted averaging (OWA) function. They are very useful for uncertain situations where the available information cannot be assessed with numerical values but it is possible to use linguistic assessments. These aggregation operators generalize a wide range of aggregation operators that use linguistic information such as the linguistic generalized mean (LGM), the linguistic OWA (LOWA) operator and the linguistic or- dered weighted quadratic averaging (LOWQA) operator. We also introduce a further generalization by using quasi-arithmetic means instead of generalized means obtaining the quasi-LWA and the quasi-LOWA operator. Finally, we develop an application of the new approach where we analyze a decision making problem regarding the selection of strategies.展开更多
The classic data envelopment analysis(DEA) model is used to evaluate decision-making units'(DMUs) efficiency under the assumption that all DMUs are evaluated with the same criteria setting. Recently, new research...The classic data envelopment analysis(DEA) model is used to evaluate decision-making units'(DMUs) efficiency under the assumption that all DMUs are evaluated with the same criteria setting. Recently, new researches begin to focus on the efficiency analysis of non-homogeneous DMU arose by real practices such as the evaluation of departments in a university, where departments argue for the adoption of different criteria based on their disciplinary characteristics. A DEA procedure is proposed in this paper to address the efficiency analysis of two non-homogeneous DMU groups. Firstly, an analytical framework is established to compromise diversified input and output(IO) criteria from two nonhomogenous groups. Then, a criteria fusion operation is designed to obtain different DEA analysis strategies. Meanwhile, Friedman test is introduced to analyze the consistency of all efficiency results produced by different strategies. Next, ordered weighted averaging(OWA) operators are applied to integrate different information to reach final conclusions. Finally, a numerical example is used to illustrate the proposed method. The result indicates that the proposed method relaxes the restriction of the classical DEA model,and can provide more analytical flexibility to address different decision analysis scenarios arose from practical applications.展开更多
On the basis of prioritized aggregated operator and prioritized ordered weighted average(POWA)operator,in this paper,the authors further present interval neutrosophic prioritized ordered weighted aggregation(INPOWA)op...On the basis of prioritized aggregated operator and prioritized ordered weighted average(POWA)operator,in this paper,the authors further present interval neutrosophic prioritized ordered weighted aggregation(INPOWA)operator with respect to interval neutrosophic numbers(INNs).Firstly,the definition,operational laws,characteristics,expectation and comparative method of INNs are introduced.Then,the INPOWA operator is developed,and some properties of the operator are analyzed.Furthermore,based on the INPOWA operator and the comparative formula of the INNs,an approach to decision making with INNs is established.Finally,an illustrative example is given to verify the developed approach and to demonstrate its practicality and effectiveness.展开更多
基金supported by the projects JC2009-00189 and A/023879/09 from the Spanish Ministry of Science and Innovation
文摘This paper introduces a new aggregation model by using induced and heavy aggregation operators in distances measures such as the Hamming distance.It is called the induced heavy ordered weighted averaging(OWA) distance(IHOWAD) operator.This paper studies some of its main properties and a wide range of particular cases such as the induced heavy OWA(IHOWA) operator,the induced OWA distance(IOWAD) operator and the heavy OWA distance(HOWAD) operator.This approach is generalized by using generalized and quasi-arithmetic means obtaining the induced generalized IHOWAD(IGHOWAD) operator and the Quasi-IHOWAD operator.An application of the new approach in a decision making problem regarding the selection of strategies is developed.
基金Supported by the National Natural Science Foundation of China(72271233),Suzhou Key Laboratory of Artificial Intelligence and Social Governance Technologies(SZS2023007)Smart Social Governance Technology and Innovative Application Platform(YZCXPT2023101)。
文摘In recent years,decision-making under uncertainty has attracted substantial attention in both academia and industry,with a growing number of organizations prioritizing decision support for talent evaluation.Vague set theory has been recognized as a powerful tool to address the ambiguity of problem parameters and manage uncertainty.This paper introduces a novel talent evaluation method that harnesses the potential of vague sets.We construct a vague set Ordered Weighted Averaging(OWA)operator for offering a robust solution to intricate decision-making problems,especially in talent evaluation.The application of the OWA operator augments the decision-making process by providing a mechanism to handle the aggregation of information in a more flexible and comprehensive manner.Experimental results show the effectiveness of the proposed method,presenting an alternative for decision-makers,aiding them in selecting their preferred choices amidst uncertainty.
基金This work is funded by Newton Institutional Links 2020-21 project:623718881,jointly by British Council and National Research Council of Thailand(www.britishcouncil.org).The corresponding author is the project PI.
文摘The problem of missing values has long been studied by researchers working in areas of data science and bioinformatics,especially the analysis of gene expression data that facilitates an early detection of cancer.Many attempts show improvements made by excluding samples with missing information from the analysis process,while others have tried to fill the gaps with possible values.While the former is simple,the latter safeguards information loss.For that,a neighbour-based(KNN)approach has proven more effective than other global estimators.The paper extends this further by introducing a new summarizationmethod to theKNNmodel.It is the first study that applies the concept of ordered weighted averaging(OWA)operator to such a problem context.In particular,two variations of OWA aggregation are proposed and evaluated against their baseline and other neighbor-based models.Using different ratios of missing values from 1%-20%and a set of six published gene expression datasets,the experimental results suggest that newmethods usually provide more accurate estimates than those compared methods.Specific to the missing rates of 5%and 20%,the best NRMSE scores as averages across datasets is 0.65 and 0.69,while the highest measures obtained by existing techniques included in this study are 0.80 and 0.84,respectively.
基金supported by the Spanish Ministry of Education(JC2009-00189)the Spanish Ministry of Foreign Affairs(A/023879/09)+1 种基金the National Natural Science Foundation of China(71071002)Academic Innovation Team of Anhui University(KJTD001B,SKTD007B)
文摘A generalization of the linguistic aggregation functions (or operators) is presented by using generalized and quasiarithmetic means. Firstly, the linguistic weighted generalized mean (LWGM) and the linguistic generalized ordered weighted averaging (LGOWA) operator are introduced. These aggregation functions use linguistic information and generalized means in the weighted average (WA) and in the ordered weighted averaging (OWA) function. They are very useful for uncertain situations where the available information cannot be assessed with numerical values but it is possible to use linguistic assessments. These aggregation operators generalize a wide range of aggregation operators that use linguistic information such as the linguistic generalized mean (LGM), the linguistic OWA (LOWA) operator and the linguistic or- dered weighted quadratic averaging (LOWQA) operator. We also introduce a further generalization by using quasi-arithmetic means instead of generalized means obtaining the quasi-LWA and the quasi-LOWA operator. Finally, we develop an application of the new approach where we analyze a decision making problem regarding the selection of strategies.
基金supported by the National Natural Science Foundation of China(71471087)
文摘The classic data envelopment analysis(DEA) model is used to evaluate decision-making units'(DMUs) efficiency under the assumption that all DMUs are evaluated with the same criteria setting. Recently, new researches begin to focus on the efficiency analysis of non-homogeneous DMU arose by real practices such as the evaluation of departments in a university, where departments argue for the adoption of different criteria based on their disciplinary characteristics. A DEA procedure is proposed in this paper to address the efficiency analysis of two non-homogeneous DMU groups. Firstly, an analytical framework is established to compromise diversified input and output(IO) criteria from two nonhomogenous groups. Then, a criteria fusion operation is designed to obtain different DEA analysis strategies. Meanwhile, Friedman test is introduced to analyze the consistency of all efficiency results produced by different strategies. Next, ordered weighted averaging(OWA) operators are applied to integrate different information to reach final conclusions. Finally, a numerical example is used to illustrate the proposed method. The result indicates that the proposed method relaxes the restriction of the classical DEA model,and can provide more analytical flexibility to address different decision analysis scenarios arose from practical applications.
基金supported by the National Natural Science Foundation of China under Grant Nos.71471172and 71271124the Humanities and Social Sciences Research Project of Ministry of Education of China under Grant No.13YJC630104+2 种基金Shandong Provincial Social Science Planning Project under Grant No.13BGLJ10the National Soft Science Research Project under Grant No.2014GXQ4D192the Natural Science Foundation of Shandong Province under Grant No.ZR2014JL046
文摘On the basis of prioritized aggregated operator and prioritized ordered weighted average(POWA)operator,in this paper,the authors further present interval neutrosophic prioritized ordered weighted aggregation(INPOWA)operator with respect to interval neutrosophic numbers(INNs).Firstly,the definition,operational laws,characteristics,expectation and comparative method of INNs are introduced.Then,the INPOWA operator is developed,and some properties of the operator are analyzed.Furthermore,based on the INPOWA operator and the comparative formula of the INNs,an approach to decision making with INNs is established.Finally,an illustrative example is given to verify the developed approach and to demonstrate its practicality and effectiveness.