Yi Peng

Analytic hierarchy process (AHP) is widely used in group decision making (GDM). There are two traditional aggregation methods for the collective preference in AHP-GDM: aggregation of the individual judgments (AIJ) and aggregation of the individual priorities (AIP). However, AHP-GDM is sometimes less reliable only under the condition of AIJ and AIP...

In the past 10 years, a large number of consensus-reaching approaches for group decision making (GDM) have been proposed. While these methods either focus on the cost of the consensus reaching or the convergency of the consensus process, the consensus efficiency has long been ignored. Meanwhile, the measurements of consensus threshold are often det...

Balancing the accuracy rates of the majority and minority classes is challenging in imbalanced classification. Furthermore, data characteristics have a significant impact on the performance of imbalanced classifiers, which are generally neglected by existing evaluation methods. The objective of this study is to introduce a new criterion to comprehe...

Pairwise comparison matrix (PCM) has been widely employed in the multi-criteria decision-making (MCDM) problems to rank the criteria and alternatives according to the considered criteria in Analytic Hierarchy Process (AHP). The PCM should have the acceptable consistency before deriving a priority vector from it. Approximate thresholds of geometric...

Technology-oriented micro and small enterprises (TMSEs) play an important role in technology innovation, employment increase, and economic growth. Due to their high risk and resource consuming nature, the Chinese government has set up special funding, and encouraged banks to provide credit support for TMSEs. However, traditional enterprises’ credit...

Coupled information diffusion in complex networks has been widely studied in recent years. Nevertheless, current research mainly focuses on the interaction between each information pair. In this study, we investigate the interaction of multiple types of information on multiplex networks by considering both the competition and the cooperation among...

Citation recommendation recommends relevant documents to users based on their inputs and other information. Many traditional citation recommendation models use keywords to describe item attributes and ignore the semantics of sequences, which cause the relevance of the search results unsatisfactory. This paper proposes a deep-learning-based dual enc...

Security against systemic financial risks is the main theme for financial stability regulation. As modern financial markets are highly interconnected and complex networks, their network resilience is an important indicator of the ability of the financial system to prevent risks. To provide a comprehensive perspective on the network resilience of fi...

Consensus reaching process (CRP) is a dynamic and interactive method used to reach a group decision. Now that social networks and mobile internet are prominent features in daily life, more experts are able to take participate in decision making in the network and their opinions are influenced by others in the decision-making process. Therefore, how...

Group decision making (GDM) is normally resource consuming and requires a moderator to lead a group of experts to achieve consensus. A moderator’s preference on consensus level affects the cost of GDM and has important impacts on the consensus results. However, no previous research has considered the moderator’s preference in consensus. The objecti...

In many financial applications, such as fraud detection, reject inference, and credit evaluation, detecting clusters automatically is critical because it helps to understand the subpatterns of the data that can be used to infer user's behaviors and identify potential risks. Due to the complexity of human behaviors and changing social environments,...

This paper considers a group decision-making mechanism for a group of social agents with ambiguous interactions. First, a fuzzy inference approach is introduced to describe bounded confidence based interaction rules among social agents for a certain object in a social network platform. Second, an influence graph is introduced to model the communica...

Studies have shown that online product reviews can indicate the position of a competitive brand. Even though reviews on different platforms may express different opinions, most studies are based on only one platform. This may lead to an inaccurate analysis of market structure. To solve this problem, we develop a novel market structure analysis base...

Urban resettlement projects involve a large number of stakeholders and impose tremendous cost. Developing resettlement plans and reaching an agreement amongst stakeholders about resettlement plans at a reasonable cost are some of the key issues in urban resettlement. From this perspective, urban resettlement is a typical large-scale group decision-...

As the number of people involved in a decision-making problem increases, the complexity of the group decision-making (GDM) process increases accordingly. The size of participants and the heterogeneous information have important effects on the consensus reaching process in GDM. To deal with these two issues, traditional methods divide large groups i...

Distance metric learning (DML) aims to learn distance metrics that reflect the interactions between features and labels. Due to the high computational complexity, existing DML models are unsuitable for large-scale datasets. This study proposes a DML approach for large-scale problems by reducing the number of variables, utilizing sparse structures o...

Pairwise comparison matrix (PCM) is an important tool to rank items by deriving priorities and has been used in various applications. Though large-scale sparse PCMs appear frequently in today's big data environment, it is hard for existing prioritization methods to handle large-scale sparse PCMs efficiently due to the curse of dimensionality. The g...

Pairwise comparison is a powerful tool in intelligent decision making systems. Items are compared using numerical judgments that estimate the item weight ratios, which are provided by decision makers or transformed by objective data. The reliable assignment of numerical judgments is important, because judgment variety leads to significantly differe...

Since the performances of suppliers usually fluctuate over time and are affected by environmental changes, enterprises need to evaluate suppliers in the past several periods, rather than a single period. As the environment changes, decision makers will exchange their views and influence each other. In addition, multiple decision makers are not alwa...

In real-life group decision-making (GDM) problems, the preferences given by decision-makers(DMs) are often incomplete, because the complexity of decision-making problems and the limitation of knowledge of DM make it difficult for DMs to take a determined evaluation of alternatives. In addition, preference relations provided by DMs are often heterog...

Many bankruptcy prediction models for small and medium-sized enterprises (SMEs) are built using accounting-based financial ratios. This study proposes a bankruptcy prediction model for SMEs that uses transactional data and payment network–based variables under a scenario where no financial (accounting) data are required. Offline and online test res...

The problem of deriving the priority vector from a pairwise comparison matrix is at the heart of multiple-criteria decision-making problems. Existing prioritization methods mostly model the inconsistency in relative preference–the ratio of two preference weights–by allowing for a small deviation, either additively or multiplicatively. In this study...

Purpose Social media commerce provides a convenient way for users to share information and interact with each other. Few studies, however, have examined the effect of marketing messages and consumer engagement behaviors on the economic performance of marketing. This study, therefore, explored the economic performance of social media in terms of mar...

In malicious URLs detection, traditional classifiers are challenged because the data volume is huge, patterns are changing over time, and the correlations among features are complicated. Feature engineering plays an important role in addressing these problems. To better represent the underlying problem and improve the performances of classifiers in...

Non-cooperative behavior is a common situation in large-scale group decision-making (LSGDM) problems. In addition, decision makers in LSGDM often use different preference formats to express their opinions, due to their educational backgrounds, knowledge, and experiences. Heterogeneous preference information and non-cooperative behaviors bring chall...

Online peer-to-peer (P2P) lending is a new form of loans. Different from traditional banks, lenders provide loans to borrowers directly through P2P platforms. Since many P2P loans are unsecured personal loans, credit rating of loans is vital to control default risk and improve profit for lenders and platforms. Standard binary classifiers are inappr...

In this paper, we propose two-stage prioritization procedure (TSPP) for multiplicative Analytic Hierarchy Process-group decision making (AHP-GDM), which involves determining the group priority vector based on the individual pair-wise comparison matrices (PCMs), simultaneously considering the consensus and consistency of the individual PCMs. The fir...

Credit rating has long been a topic of interest in academic research. There are lots of studies about credit rating methods for large and listed companies. However, due to the lack of financial data and information asymmetry, developing credit ratings for small and medium-sized enterprises (SMEs) is difficult. To alleviate this problem, this paper...

Urban buses usually require several distinct types of preventive maintenance. Due to the complexity of urban buses, quantifying the efficiency of preventive maintenance is very important for maintenance management decision-making and optimization. This study presents a sequential imperfect preventive maintenance model to quantify the maintenance ef...

The evaluation of feature selection methods for text classification with small sample datasets must consider classification performance, stability, and efficiency. It is, thus, a multiple criteria decision-making (MCDM) problem. Yet there has been few research in feature selection evaluation using MCDM methods which considering multiple criteria. T...

The time-dependent vehicle routing problem with time windows (TDVRPTW) is examined in this study. A mathematical model for the TDVRPTW is formulated by considering the time-dependent vehicle speeds, capacity, travel time, wait time, customer demand, service time, time window, impact of dynamic loads, and travel speed effect on vehicle carbon emissi...

To address the shortage of relief in disaster areas during the early stages after an earthquake, a location-routing problem (LRP) was studied from the perspective of fairness. A multi-objective model for the fair LRP was developed by lexicographic order object optimal method in consideration of the urgent window constraints, partial road damage, mu...

Financial systemic risk is an important issue in economics and financial systems. Trying to detect and respond to systemic risk with growing amounts of data produced in financial markets and systems, a lot of researchers have increasingly employed machine learning methods. Machine learning methods study the mechanisms of outbreak and contagion of s...

Trade-based Money Laundering, a new form of money laundering using international trade as a signboard, always appears along with speculative capital movement which has been accepted as the most concerned and consensus incentive giving rise to the collapse of the financial market. Unfortunately, preventing money laundering is very difficult since mo...

Previous studies have demonstrated that online reviews play an important role in the purchase decision process. Though the effects of positive and negative reviews to consumers’ purchase decisions have been analyzed, they were examined statically and separately. In reality, online review community allows everyone to express and receive opinions and...

Nowadays, datasets are always dynamic and patterns in them are changing. Instances with different labels are intertwined and often linearly inseparable, which bring new challenges to traditional learning algorithms. This paper proposes adaptive hyper-sphere (AdaHS), an adaptive incremental classifier, and its kernelized version: Nys-AdaHS. The clas...

In this paper, from the perspective of opinion dynamics theory, we investigate the interaction mechanism of a group of autonomous agents in an e-commerce community (or social network), and the influence power of opinion leaders during the formation of group opinion. According to the opinion's update manner and influence, this paper divides social a...

Ranking historical players in sports is challenging since some players have never played against each other. It is even more complex in Go because of AlphaGo, a project based on artificial intelligence, who became the world's number 1 after it defeated the 528th and the 4th human Go players. AlphaGo is ranked high in the current Go ranking system b...

In today's large-scaled distributed learning, it often involves a large number of machines. Coordination between them can be very complicated. In order to emphasize the importance of the organic relationships between machines, we introduce the organization theories of human society, such as cooperation and competition, to machine learning. We desig...

This paper proposes a group decision-making (GDM) method for integrating heterogeneous information. To avoid information loss, instead of transforming heterogeneous information into a single form, the proposed method integrates heterogeneous information using a weighted-power average operator. The consensus degree between the individual-decision ma...

A financial market is a complex, dynamic system with an underlying governing manifold. This study introduces an early warning method for financial markets based on manifold learning. First, we restructure the phase space of a financial system using financial time series data. Then, we propose an information metric-based manifold learning (IMML) alg...

Supplier selection plays an important role in supply chain system. In order to select the suitable suppliers, some methods of supplier selection have been studied extensively in fuzzy environment. In this paper, a fuzzy information fusion approach based on generalized fuzzy numbers (GFNs) is proposed to select the best supplier. Some aggregation op...

Unconventional emergency decision making not only involves intangible and conflicting criteria, but also needs a fast response to the emergency incident under the cases of time pressure and incomplete information. It might be an effective way to make full use of the outlier data of incident information and skip some direct comparisons between alter...

Timely response is extremely important in emergency management. However, cardinal inconsistent data may exist in a judgment matrix because of the limited expertise , preference conflict as well as the complexity nature of the decision problems. The existing inconsistent data processing models for positive reciprocal matrix either are complicated or...

Post-seismic inventory and logistics planning under incomplete and fuzzy information is an important yet understudied area in supply chain risk management. The goal of this paper is to propose a system dynamics model to analyze the behaviors of disrupted disaster relief supply chain by simulating the uncertainties associated with predicting post-se...

Software packages evaluation and selection is one of the most important activities encountered by software as a service (SaaS) users in the high performance networked computing environment, especially for the small or medium-sized enterprises. In this paper, we propose a framework for SaaS software packages evaluation and selection by combining the...

One of the critical challenges in incident management is to provide timely response. Therefore, in this paper, a modified PROMETHEE II (Preference Ranking Organization Method for Enrichment Evaluation) is proposed to improve the efficiency and response time in incident management. Specifically, since the computing time and computation complexity of...

This paper proposes a dominant-submissive agent model on bounded confidence opinion dynamics under an emergency environment. In the proposed model, environmental noises and opinion leaders are involved in the collective opinion formation. A series of computer simulations demonstrate that environmental noises have a great impact on the collective op...

In this research, a mathematics model is proposed to describe the mission availability for bounded-cumulative-downtime system. In the proposed model, the cumulative downtime and cumulative uptime are considered as constraints simultaneously. The mission availability can be defined as the probability that all repairs do not exceed the bounded cumula...

Resource allocation is a complicated task in cloud computing environment because there are many alternative computers with varying capacities. The goal of this paper is to propose a model for task-oriented resource allocation in a cloud computing environment. Resource allocation task is ranked by the pairwise comparison matrix technique and the Ana...

Questionnaire survey is a commonly used way to collect opinions and views in AHP/ANP. However, many factors such as tedious design format, redundant content, long length etc, may lead to inconsistent comparison matrix for the decision problem. Invalid or bad results of a questionnaire survey may cause the decision makers to make wrong decision. Fur...

In Chap. 3, the induced bias matrix is proposed to identify the inconsistent elements in a complete pairwise comparison matrix (PCM). Besides inconsistency, a PCM may be incomplete due to limited expertise or unwillingness to judge. For an incomplete pairwise comparison matrix (IPCM), the missing values must first be estimated in order for the IPCM...

When a new alternative or criterion is added to the decision model or old ones are deleted from the decision matrix, the rank of the alternatives may be reversed, namely, a less preferred alternative may become more preferred. In this Chapter, the IBMM is further extended to perform the sensitivity analysis of rank reversal when a new alternative o...

The positive reciprocal pairwise comparison matrix (PCM) is one of the key components which is used to quantify the qualitative and/or intangible attributes into measurable quantities. This book examines six understudied issues of PCM, i.e. consistency test, inconsistent data identification and adjustment, data collection, missing or uncertain data...

The consistency test is one of the critical components both in AHP and ANP. Currently, the consistency ratio (CR) proposed by Saaty is popularly used to test the consistencies of the pairwise comparison matrices. However, when the number of comparison matrices increases, the consistency test of comparison matrices both in the AHP and ANP becomes co...

As stated previously, the inconsistent elements should be identified if the pairwise comparison matrix (PCM) failed to the consistency test, therefore, the methods for identifying and adjusting the inconsistent elements in the PCM have been extensively studied since the AHP/ANP were developed by Saaty. However, existing methods are either too compl...

In previous Chapters, IBMM and its related extensions and applications are presented. In Ergu and Kou (2012), another form of induced bias matrix model, induced arithmetic average bias matrix model (IAABMM), is proposed and proved mathematically, which is easier to be understood than the previous model. In addition, two simpler inconsistency identi...

This paper proposes a multiple criteria decision making (MCDM)-based framework to address two fundamental issues in cluster validation: 1) evaluation of clustering algorithms and 2) estimation of the optimal cluster number for a given data set. Since both issues involve more than one criterion, they can be modeled as multiple criteria decision maki...

Opinion dynamics focuses on the opinion evolution in a social community. Recently, some models of continuous opinion dynamics under bounded confidence were proposed by Deffuant and Krause, et al. In the literature, agents were generally assumed to have a homogeneous confidence level. This paper proposes an extended model for a group of agents with...

Mining data streams with concept drifts is always an important and challenge task for researchers in both application and theory areas, such as emergency management. Because of requiring massive training data with labels, it is a hard and time costing work for existing (ensemble) classical models, sometimes even impossible. Aim to resolve this issu...

In this paper, two improved MCDM models including Analytic Hierarchy Process (AHP) for group decision-making and revised TOPSIS model are applied for credit risk evaluation. The index weight is determined by AHP for group decision-making, which can establish the decision matrix with less subjective judgments and improve the accuracy of index weight...

Determining the number of clusters in a data set is an essential yet difficult step in cluster analysis. Since this task involves more than one criterion, it can be modeled as a multiple criteria decision making (MCDM) problem. This paper proposes a multiple criteria decision making (MCDM)-based approach to estimate the number of clusters for a giv...

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...

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 clas...

The vulnerability assessment is important for earthquake prevention and mitigation. Since many criteria need to be considered during the evaluation process, it can be modeled as a multiple criteria decision making (MCDM) problem. This paper proposes an approach which integrates the results of different MCDM methods to provide regional earthquake vu...

Various methods and algorithms have been developed for multiclass classification problems in recent years. How to select an effective algorithm for a multiclass classification task is an important yet difficult issue. Since the multiclass algorithm selection normally involves more than one criterion, such as accuracy and computation time, the selec...

Tests of consistency for the pair-wise comparison matrices have been studied extensively since AHP was introduced by Saaty in 1970s. However, existing methods are either too complicated to be applied in the revising process of the inconsistent comparison matrix or are difficult to preserve most of the original comparison information due to the use...