The Experts below are selected from a list of 9612 Experts worldwide ranked by ideXlab platform
Enrique Herreraviedma - One of the best experts on this subject based on the ideXlab platform.
-
multiple Attribute strategic Weight manipulation with minimum cost in a group decision making context with interval Attribute Weights information
IEEE Transactions on Systems Man and Cybernetics, 2019Co-Authors: Yating Liu, Yucheng Dong, Haiming Liang, Francisco Chiclana, Enrique HerreraviedmaAbstract:In multiple Attribute decision making (MADM), strategic Weight manipulation is understood as a deliberate manipulation of Attribute Weight setting to achieve a desired ranking of alternatives. In this paper, we study the strategic Weight manipulation in a group decision making (GDM) context with interval Attribute Weight information. In GDM, the revision of the decision makers’ original Attribute Weight information implies a cost. Driven by a desire to minimize the cost, we propose the minimum cost strategic Weight manipulation model, which is achieved via optimization approach, with the mixed 0–1 linear programming model being proved appropriate in this context. Meanwhile, some desired properties to manipulate a strategic Attribute Weight based on the ranking range under interval Attribute Weight information are proposed. Finally, numerical analysis and simulation experiments are provided with a twofold aim: 1) to verify the validity of the proposed models and 2) to show the effects of interval Attribute Weights information and the unit cost, respectively, on the cost to manipulate strategic Weights in the MADM in a group decision context.
-
strategic Weight manipulation in multiple Attribute decision making
Omega-international Journal of Management Science, 2018Co-Authors: Yucheng Dong, Yating Liu, Haiming Liang, Francisco Chiclana, Enrique HerreraviedmaAbstract:In some real-world multiple Attribute decision making (MADM) problems, a decision maker can strategically set Attribute Weights to obtain her/his desired ranking of alternatives, which we call the strategic Weight manipulation of the MADM. In this paper, we define the concept of the ranking range of an alternative in the MADM, and propose a series of mixed 0–1 linear programming models (MLPMs) to show the process of designing a strategic Attribute Weight vector. Then, we reveal the conditions to manipulate a strategic Attribute Weight based on the ranking range and the proposed MLPMs. Finally, a numerical example with real background is used to demonstrate the validity of our models, and simulation experiments are presented to show the better performance of the ordered Weighted averaging operator than the Weighted averaging operator in defending against the strategic Weight manipulation of the MADM problems.
Chenhao Liu - One of the best experts on this subject based on the ideXlab platform.
-
an Attribute Weight assignment and particle swarm optimization algorithm for medical database classifications
Computer Methods and Programs in Biomedicine, 2012Co-Authors: Peichann Chang, Jyunjie Lin, Chenhao LiuAbstract:In this research, a hybrid model is developed by integrating a case-based reasoning approach and a particle swarm optimization model for medical data classification. Two data sets from UCI Machine Learning Repository, i.e., Liver Disorders Data Set and Breast Cancer Wisconsin (Diagnosis), are employed for benchmark test. Initially a case-based reasoning method is applied to preprocess the data set thus a Weight vector for each feature is derived. A particle swarm optimization model is then applied to construct a decision-making system for diseases identified. The PSO algorithm starts by partitioning the data set into a relatively large number of clusters to reduce the effects of initial conditions and then reducing the number of clusters into two. The average forecasting accuracy for breast cancer of CBRPSO model is 97.4% and for liver disorders is 76.8%. The proposed case-based particle swarm optimization model is able to produce more accurate and comprehensible results for medical experts in medical diagnosis.
Yucheng Dong - One of the best experts on this subject based on the ideXlab platform.
-
multiple Attribute strategic Weight manipulation with minimum cost in a group decision making context with interval Attribute Weights information
IEEE Transactions on Systems Man and Cybernetics, 2019Co-Authors: Yating Liu, Yucheng Dong, Haiming Liang, Francisco Chiclana, Enrique HerreraviedmaAbstract:In multiple Attribute decision making (MADM), strategic Weight manipulation is understood as a deliberate manipulation of Attribute Weight setting to achieve a desired ranking of alternatives. In this paper, we study the strategic Weight manipulation in a group decision making (GDM) context with interval Attribute Weight information. In GDM, the revision of the decision makers’ original Attribute Weight information implies a cost. Driven by a desire to minimize the cost, we propose the minimum cost strategic Weight manipulation model, which is achieved via optimization approach, with the mixed 0–1 linear programming model being proved appropriate in this context. Meanwhile, some desired properties to manipulate a strategic Attribute Weight based on the ranking range under interval Attribute Weight information are proposed. Finally, numerical analysis and simulation experiments are provided with a twofold aim: 1) to verify the validity of the proposed models and 2) to show the effects of interval Attribute Weights information and the unit cost, respectively, on the cost to manipulate strategic Weights in the MADM in a group decision context.
-
strategic Weight manipulation in multiple Attribute decision making
Omega-international Journal of Management Science, 2018Co-Authors: Yucheng Dong, Yating Liu, Haiming Liang, Francisco Chiclana, Enrique HerreraviedmaAbstract:In some real-world multiple Attribute decision making (MADM) problems, a decision maker can strategically set Attribute Weights to obtain her/his desired ranking of alternatives, which we call the strategic Weight manipulation of the MADM. In this paper, we define the concept of the ranking range of an alternative in the MADM, and propose a series of mixed 0–1 linear programming models (MLPMs) to show the process of designing a strategic Attribute Weight vector. Then, we reveal the conditions to manipulate a strategic Attribute Weight based on the ranking range and the proposed MLPMs. Finally, a numerical example with real background is used to demonstrate the validity of our models, and simulation experiments are presented to show the better performance of the ordered Weighted averaging operator than the Weighted averaging operator in defending against the strategic Weight manipulation of the MADM problems.
Peichann Chang - One of the best experts on this subject based on the ideXlab platform.
-
an Attribute Weight assignment and particle swarm optimization algorithm for medical database classifications
Computer Methods and Programs in Biomedicine, 2012Co-Authors: Peichann Chang, Jyunjie Lin, Chenhao LiuAbstract:In this research, a hybrid model is developed by integrating a case-based reasoning approach and a particle swarm optimization model for medical data classification. Two data sets from UCI Machine Learning Repository, i.e., Liver Disorders Data Set and Breast Cancer Wisconsin (Diagnosis), are employed for benchmark test. Initially a case-based reasoning method is applied to preprocess the data set thus a Weight vector for each feature is derived. A particle swarm optimization model is then applied to construct a decision-making system for diseases identified. The PSO algorithm starts by partitioning the data set into a relatively large number of clusters to reduce the effects of initial conditions and then reducing the number of clusters into two. The average forecasting accuracy for breast cancer of CBRPSO model is 97.4% and for liver disorders is 76.8%. The proposed case-based particle swarm optimization model is able to produce more accurate and comprehensible results for medical experts in medical diagnosis.
Yating Liu - One of the best experts on this subject based on the ideXlab platform.
-
multiple Attribute strategic Weight manipulation with minimum cost in a group decision making context with interval Attribute Weights information
IEEE Transactions on Systems Man and Cybernetics, 2019Co-Authors: Yating Liu, Yucheng Dong, Haiming Liang, Francisco Chiclana, Enrique HerreraviedmaAbstract:In multiple Attribute decision making (MADM), strategic Weight manipulation is understood as a deliberate manipulation of Attribute Weight setting to achieve a desired ranking of alternatives. In this paper, we study the strategic Weight manipulation in a group decision making (GDM) context with interval Attribute Weight information. In GDM, the revision of the decision makers’ original Attribute Weight information implies a cost. Driven by a desire to minimize the cost, we propose the minimum cost strategic Weight manipulation model, which is achieved via optimization approach, with the mixed 0–1 linear programming model being proved appropriate in this context. Meanwhile, some desired properties to manipulate a strategic Attribute Weight based on the ranking range under interval Attribute Weight information are proposed. Finally, numerical analysis and simulation experiments are provided with a twofold aim: 1) to verify the validity of the proposed models and 2) to show the effects of interval Attribute Weights information and the unit cost, respectively, on the cost to manipulate strategic Weights in the MADM in a group decision context.
-
strategic Weight manipulation in multiple Attribute decision making
Omega-international Journal of Management Science, 2018Co-Authors: Yucheng Dong, Yating Liu, Haiming Liang, Francisco Chiclana, Enrique HerreraviedmaAbstract:In some real-world multiple Attribute decision making (MADM) problems, a decision maker can strategically set Attribute Weights to obtain her/his desired ranking of alternatives, which we call the strategic Weight manipulation of the MADM. In this paper, we define the concept of the ranking range of an alternative in the MADM, and propose a series of mixed 0–1 linear programming models (MLPMs) to show the process of designing a strategic Attribute Weight vector. Then, we reveal the conditions to manipulate a strategic Attribute Weight based on the ranking range and the proposed MLPMs. Finally, a numerical example with real background is used to demonstrate the validity of our models, and simulation experiments are presented to show the better performance of the ordered Weighted averaging operator than the Weighted averaging operator in defending against the strategic Weight manipulation of the MADM problems.
