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Wenshuai Wu

Bio: Wenshuai Wu is an academic researcher from Sichuan University. The author has contributed to research in topics: Analytic hierarchy process & Multiple-criteria decision analysis. The author has an hindex of 7, co-authored 10 publications receiving 317 citations. Previous affiliations of Wenshuai Wu include University of Electronic Science and Technology of China & East China Jiaotong University.

Papers
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Journal ArticleDOI
TL;DR: This study compares experimentally the performance of several popular ensemble methods using 13 different performance metrics over 10 public-domain software defect datasets from the NASA Metrics Data Program (MDP) repository and indicates that ensemble methods can improve the classification results of software defect prediction in general and AdaBoost gives the best results.
Abstract: Classification algorithms that help to identify software defects or faults play a crucial role in software risk management. Experimental results have shown that ensemble of classifiers are often mo...

174 citations

Journal ArticleDOI
TL;DR: In this paper, a consensus model for group decision-making based on the analytic hierarchy process (AHP) is developed to gather group ideas and analyze the real estate investment environment under multi-criteria problems.
Abstract: Individual decision-making largely influences the effectiveness of decisions and benefits of investments. Methods: In this article, a consensus model for group decision-making (GDM), based on the analytic hierarchy process (AHP), is developed to gather group ideas and analyze the real estate investment environment under multi-criteria problems. Twelve evaluation procedures of the developed model, which increase the convergence of the opinions of multiple experts, are proposed. An empirical case about the real estate investment environment is applied to certify the feasibility of this developed model. the evaluation procedures have been fully observed with several rounds of discussions, and have manifested the experiences of experts. Besides, the evaluation results are in accordance with real-world situations, which demonstrates that our developed model is a feasible analysis tool for real estate investors to obtain better profits and lower risk.

66 citations

Journal ArticleDOI
TL;DR: In this paper, an improved Analytical Hierarchy Process-group decision making (IAHP-GDM) model is proposed to reduce investment risk, which applies the method of least squares to adjust group decision matrix to satisfy the property of positive reciprocal matrix in AHP.
Abstract: Investment strategy selection relies heavily on personal experience and behavior. This paper proposes an improved Analytical Hierarchy Process-group decision making (IAHP-GDM) model to reduce investment risk. This model applies the method of least squares to adjust group decision matrix in order to satisfy the property of positive reciprocal matrix in AHP. In addition, five experts from related fields are invited to evaluate investment risk that takes group wisdom to eliminate personal bias. An empirical study is conducted to compare the proposed model to AHP for group decision making model. The results show that the IAHP-GDM model is not only accurate and effective, but also consistent with realistic investment environment.

64 citations

Journal ArticleDOI
01 Jan 2016-Filomat
TL;DR: Wang et al. as discussed by the authors investigated the analytic hierarchy process (AHP) as a method of measuring index weights for group decision-making (GDM), taking into full account the cognitive levels of different experts.
Abstract: Credit risk analysis is a core research issue in the field of financial risk management. This paper first investigates the analytic hierarchy process (AHP) as a method of measuring index weights for group decision-making (GDM). AHP for group decision-making (AHP-GDM) is then researched and applied, taking into full account the cognitive levels of different experts. Second, the concept of grey relational degree is introduced into the ideal solution of the technique for order of preference by similarity to ideal solution (TOPSIS). This concept fully considers the relative closeness of grey relational degree between alternatives and the ”ideal” solution in order to strengthen their relationship. The AHP-GDM method overcomes the problem of subjectivity in measuring index weights, and the revised TOPSIS (R-TOPSIS) method heightens the effectiveness of assessment results. An illustrative case using data from Chinese listed commercial banks shows that the R-TOPSIS method is more effective than both TOPSIS and grey relational analysis (GRA) in credit risk evaluation. The two improved multi-criteria decision making (MCDM) methods are also applied to empirical research regarding the credit risk analysis of Chinese urban commercial banks. The results indicate the validity and effectiveness of both methods.

20 citations

Journal ArticleDOI
TL;DR: A new consensus facilitation model for AHP group decision-making that takes into account experts’ weights, called EWAHP-GDM, is established to address the problem of how to effectively aggregate individual preferences to reach a group consensus.
Abstract: In this paper, we investigate group decision-making in a multi-criteria complex environment using an analytical hierarchy process (AHP) seeking to solve the problem of how to effectively aggregate ...

17 citations


Cited by
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01 Jan 2002

9,314 citations

Journal ArticleDOI
TL;DR: The goal of this paper is to propose an approach to resolve disagreements among MCDM methods based on Spearman's rank correlation coefficient, and the experimental results prove that the proposed approach can resolve conflicting M CDM rankings and reach an agreement among different MCDm methods.
Abstract: Classification algorithm selection is an important issue in many disciplines. Since it normally involves more than one criterion, the task of algorithm selection can be modeled as multiple criteria decision making (MCDM) problems. Different MCDM methods evaluate classifiers from different aspects and thus they may produce divergent rankings of classifiers. The goal of this paper is to propose an approach to resolve disagreements among MCDM methods based on Spearman's rank correlation coefficient. Five MCDM methods are examined using 17 classification algorithms and 10 performance criteria over 11 public-domain binary classification datasets in the experimental study. The rankings of classifiers are quite different at first. After applying the proposed approach, the differences among MCDM rankings are largely reduced. The experimental results prove that the proposed approach can resolve conflicting MCDM rankings and reach an agreement among different MCDM methods.

490 citations

Journal ArticleDOI
TL;DR: A cosine maximization method (CM) based on similarity measure is proposed, which maximizes the sum of the cosine of the angle between the priority vector and each column vector of a PCM to derive the reliable priority vector.

304 citations

Journal ArticleDOI
TL;DR: A generalized soft cost consensus model under a certain degree of consensus is developed, which is built by defining a consensus level function and a generalized aggregation operator and is applied to a loan consensus scenario using data from an online peer-to-peer lending platform.

268 citations