Evidential Reasoning Algorithm Research Articles

A novel Multi-Criteria Decision Making framework based on Evidential Reasoning dealing with missing information from online reviews

Online reviews are a valuable tool for Multi-Criteria Decision Making (MCDM) because they provide insightful information on several aspects. However, not all reviews yield complete information on all the necessary criteria for the MCDM problem. In practice, users might not effectively analyze all of them due to a lack of knowledge or time constraints. Despite this shortcoming, these online reviews still offer useful information that would be lost if neglected. Therefore, this paper proposes a framework that offers a new approach to using online reviews in MCDM and effectively addresses missing information on a given criterion or criteria. This new approach first establishes an information processing mechanism to transform online reviews into Probabilistic Linguistic Term Sets (PLTSs) with interval uncertainty. Afterward, a weight determination model is defined to calculate the criteria weights, which are given as interval values to reflect the uncertainty of the criteria and consider information from online reviews to better capture the opinions of reviewers. Next, the Evidential Reasoning (ER) algorithm is extended to the PLTSs with interval uncertainty environment to fuse the evaluation information and generate the interval-valued expected utilities of the alternatives by considering the uncertainty related to the missing information. Finally, a case study is conducted to illustrate the implementation process of the proposed framework, and a comparative analysis is carried out to evaluate its performance.

A Multiattribute Group Decision-Making Method With Probabilistic Linguistic Information Based on an Adaptive Consensus Reaching Model and Evidential Reasoning.

This article proposes a new multiattribute group decision-making (MAGDM) method with probabilistic linguistic information that considers the following three aspects: an allocation of ignorance information, a realization of group consensus, and an aggregation of assessments. To allocate ignorance information, an optimization model based on minimizing the distances among experts is developed. To measure the consensus degree, a consensus index that considers the information granules of linguistic terms (LTs) is defined. On this basis, a suitable optimization model is established to realize the group consensus adaptively by optimizing the allocation of information granules of LTs with the particle swarm optimization (PSO) algorithm. With an objective to reduce the information loss during aggregation phases, the process of generating comprehensive assessments of alternatives with the evidential reasoning (ER) algorithm is presented. Therefore, a new method is developed based on the adaptive consensus reaching (ACR) model and the ER algorithm. Finally, the applicability of the proposed method is demonstrated by solving a selection problem of a financial technology company. Comparative analyses are conducted to show the advantages of the proposed method.

Investigations of Symmetrical Incomplete Information Spreading in the Evidential Reasoning Algorithm and the Evidential Reasoning Rule via Partial Derivative Analysis

Incomplete information causes great uncertainty in decision making. It is a critical task to understand how incomplete information spreads symmetrically in order to make comprehensive and balanced decisions. A better understanding of the spreading of incomplete information can also be used for accurately locating limited resources to reduce incomplete information in the input for the final purpose of reducing incomplete information in the result. In this study, the way in which incomplete information spreads is studied via the evidential reasoning (ER) algorithm and the evidential reasoning rule (the ER rule), which are known for their transparent analytical procedures. Specifically, the partial derivative analysis is conducted using the steps of ER and the ER rule for calculating the contributions made by the beliefs, weights, and reliability to the incomplete information in the result. The major theoretical contribution of this study is the calculation of the contribution of the input to the incomplete information in the output based on partial derivative analysis. A numerical case is studied to demonstrate the proposed derivative analysis, the contribution calculation, and the consequential results.

A New Decision-Making Framework for Site Selection of Electric Vehicle Charging Station With Heterogeneous Information and Multigranular Linguistic Terms

As a key technology to perform sustainable development in transportation, electric vehicles have been largely welcomed due to their advantages in energy savings and low carbon emission. The main step in promoting these vehicles is the selection of the appropriate electric vehicle charging station (EVCS) site. EVCS site selection is a laborious task because it involves a series of conflicting quantitative and qualitative criteria from several dimensions. The quantitative criteria are usually expressed by numerical data, while qualitative criteria are commonly represented by linguistic terms. Furthermore, the linguistic terms generated by different decision makers are usually defined on multigranular linguistic term sets. In this article, we present a new decision-making framework to select sustainable EVCS sites within the context of heterogeneous information and multigranular linguistic terms. First, three information transformation mechanisms are defined to unify the heterogeneous information and multigranular linguistic terms into interval-valued belief structures. Afterward, shadowed sets theory is utilized to reflect the personalized individual semantics of linguistic terms. Then, with the aid of the evidential reasoning (ER) algorithm, a new information fusion method is proposed to generate the interval-valued expected utilities of alternatives. Subsequently, an improved minimax regret approach is developed to compare and rank the interval-valued expected utilities. The proposed decision-making framework is then implemented to solve a case study for EVCS site selection. Further analysis and comparisons with other methods are also conducted to show the applicability and feasibility of the current proposal.

A New Model for Network Security Situation Assessment of the Industrial Internet

To address the problem of network security situation assessment in the Industrial Internet, this paper adopts the evidential reasoning (ER)algorithm and belief rule base (BRB) method to establish an assessment model. First, this paper analyzes the influencing factors of the Industrial Internet and selects evaluation indicators that contain not only quantitative data but also qualitative knowledge. Second, the evaluation indicators are fused with expert knowledge and the ER algorithm. According to the fusion results, a network security situation assessment model of the Industrial Internet based on the ER and BRB method is established, and the projection covariance matrix adaptive evolution strategy (P-CMA-ES) is used to optimize the model parameters. This method can not only utilize semiquantitative information effectively but also use more uncertain information and prevent the problem of combinatorial explosion. Moreover, it solves the problem of the uncertainty of expert knowledge and overcomes the problem of low modeling accuracy caused by insufficient data. Finally, a network security situation assessment case of the Industrial Internet is analyzed to verify the effectiveness and superiority of the method. The research results show that this method has strong applicability to the network security situation assessment of complex Industrial Internet systems. It can accurately reflect the actual network security situation of Industrial Internet systems and provide safe and reliable suggestions for network administrators to take timely countermeasures, thereby improving the risk monitoring and emergency response capabilities of the Industrial Internet.

Functional importance evaluation approach for cloud manufacturing services based on complex network and evidential reasoning rule

In the uncertain cloud environment, manufacturing services monitoring is effective to ensure the normal function of cloud manufacturing service system (CMSS). However, continuously monitoring all of the manufacturing services is impractical since it is resource consuming. One feasible method is to prioritize the allocation of monitoring resources to the important services. Therefore, we propose an approach for evaluating the functional importance of manufacturing services based on complex network and evidential reasoning (ER) rule. Firstly, a domain-oriented cloud manufacturing service complex network (DoCMSCN) model is constructed and elaborated. Secondly, based on the idea of multi-granularity and multi-indicator, an evaluation model for the importance of manufacturing services is presented. Different centrality indicators of the DoCMSCN in different functional granularities are obtained and transformed into evaluation evidence. Then the ER algorithm is applied to fused the evaluation results. In the fusion process, the reliability and weight of each piece of evidence are fully considered to improve the fusion accuracy. The experimental results of vertical elevator design services show the proposed approach can effectively find the important manufacturing services and superior than the existing ones. It can facilitate the decision-making of monitoring strategy in conditions of limited resources from the functional perspective. Finally, we develop a prototype monitoring system for vertical elevator design services.

Vulnerability assessment of strait/canals in maritime transportation using fuzzy evidential reasoning approach.

The safety and security of straits and canals have been playing an important role in maritime transportation. The disruption of a strait or canal will lead to increased transportation costs and world trade problems. Therefore, an advanced approach incorporating fuzzy logic and an evidential reasoning (ER) algorithm is developed to conduct the vulnerability assessment of straits or canals in this paper. A hierarchical structure is first developed taking into account both qualitative and quantitative factors. The fuzzy rule-based transformation technique is applied to convert quantitative factors into qualitative ones, which enables the application of a fuzzy ER method to synthesize all the information from the bottom to the top along the developed hierarchical structure. The software of intelligent decision system (IDS) is used to facilitate the process of vulnerability assessment. The developed framework then is validated and demonstrated in a case study for vulnerability prioritization which can be used as a reference to ensure the safety and security of straits and canals for decision-makers.

A new unmanned aerial vehicle intrusion detection method based on belief rule base with evidential reasoning

With the growing security demands in the public, civil and military fields, unmanned aerial vehicle (UAV) intrusion detection has attracted increasing attention. In view of the shortcomings of the current UAV intrusion detection model using Wi-Fi data traffic in terms of detection accuracy, sample size reduction, and model interpretability, this paper proposes a new detection algorithm for UAV intrusion. This paper presents an interpretable intrusion detection model for UAVs based on the belief rule base (BRB). BRB can effectively use various types of information to establish any nonlinear relationship between the model input and output. It can model and simulate any nonlinear model and optimize the model parameters. However, the rule combination explosion problem is encountered in BRB if there are too many attributes. Therefore, an evidential reasoning (ER) algorithm is proposed for solving this problem. By combining the capabilities of the ER and the BRB methodologies, a new evaluation model, named the EBRB-based model, is proposed here for predicting UAV intrusion detection, even in the case of a massive number of attributes. The global optimization of the model is ensured. A new interpretable and globally optimized UAV intrusion detection model is proposed, which is the main contribution of this paper. An experimental case is used to demonstrate the implementation and application of the proposed UAV intrusion detection method.

Evaluation of Student-Perceived Service Quality in Higher Education for Sustainable Development: A Fuzzy TODIM-ERA Method

Evaluating and prioritizing the service quality of higher education is an essential issue for the successful implementation of Education for Sustainable Development (ESD). This study investigates an evaluation framework to assess the performances of higher education institutes (HEIs) within the context of ESD based on student-perceived service quality. First, a conceptual model of the evaluation indicator system is explored by embedding sustainability-related indicators into the fuzzy SERVQUAL scale. Then, the evaluation of student-perceived service quality can be thought of as a problem of multicriteria decision-making (MCDM) that involves uncertainty and bounded rationality. Thus, an evaluation technique called hybrid fuzzy TODIM-ERA is proposed to address such evaluation problems by synthesizing the theoretical strengths of the intuitionistic fuzzy set theory, the evidential reasoning algorithm (ERA), and the TODIM (an acronym in Portuguese for interactive and multicriteria decision-making). Finally, a case study of five Chinese HEIs in maritime transportation is used to demonstrate the effectiveness and robustness of the proposed framework. Results provide the ranking order of all the alternative HEIs and the improvement strategies of each HEI for student-perceived service quality dimensions.

A New Fault Diagnosis Model for Circuits in Railway Vehicle Based on the Principal Component Analysis and the Belief Rule Base

Circuits are considered an important part of railway vehicles, and circuit fault diagnosis in the railway vehicle is also a research hotspot. In view of the nonlinearity and diversity of track circuit components, as well as the diversity and similarity of fault phenomena, in this paper, a new fault diagnosis model for circuits based on the principal component analysis (PCA) and the belief rule base (BRB) is proposed, which overcomes the shortcomings of the circuit fault diagnosis method based on data, model, and knowledge. In the proposed model, to simplify the model and improve the accuracy, PCA is used to reduce the dimension of the key fault features, and varimax rotation is used to deduce the fault features. BRB is used to combine qualitative knowledge and quantitative data effectively, and evidential reasoning (ER) algorithm is used to carry out the inference of knowledge. The initial parameters of the model are optimized, and the optimal precondition attributes, rule weights, and belief degree parameters are obtained to improve the accuracy. Through the training and testing of the model, the experimental results show that the method can accurately diagnose the fault of the driver controller potentiometer in the railway vehicle. Compared with other methods, the model shows high accuracy.

Towards achieving consistent opinion fusion in group decision making with complete distributed preference relations

Belief distribution (BD) is a scheme of representing qualitative information with subjective uncertainty and imprecision. Distributed preference relation (DPR) extends BDs to the form of pairwise comparison by expressing the preferred, non-preferred, indifferent, and uncertain degrees of one decision alternative over another. However, previous studies on DPR only require comparison of adjacent alternatives, and consensus reaching is not considered fully in the decision making process. To solve this problem, a complete DPR model is presented in this paper to support group decision making (GDM). First, a consistency index is defined to measure the consistency level of the complete DPR representing experts’ judgments. Second, an automatic adjustment algorithm is proposed to improve the consistency of DPRs with unacceptable consistency to an acceptable level. Third, the evidential reasoning (ER) algorithm is utilized to aggregate all the DPRs together, and an optimization model is further constructed to generate experts’ weights, which maximizes the degree of consensus among experts. A GDM example is provided to illustrate the applicability and validity of the proposed DPR model, and comparative analysis demonstrates the potential of the proposed method in supporting real-world GDM problems.

Decision analysis framework based on incomplete online textual reviews

Information is a key factor that influences the performance of decision makers. With the explosive proliferation of Web 2.0, the volume of online textual reviews has been sharply increasing. However, how to use this type of unstructured data and utilize the valuable information hidden behind are still problems to be solved. This study aims to provide a multi-attributes decision analysis (MADA) framework based on incomplete online textual reviews to aid in decision making. First, online textual reviews are obtained by data crawling. Attributes information is determined by textual analysis and the attitudes of assessors toward each attribute is discriminated by the sentiment analysis. Then, some new rules are developed in encoding incomplete online textual reviews into interval-valued linguistic distribution assessment (ILDA) to better characterize the evaluators’ attitudes. Next, evidential reasoning (ER) algorithm is extended to the ILDA environment to combine the information with multiple attributes, and the utility interval of each alternative is constructed by solving a pair of nonlinear optimization models. Given that the interval data cannot be directly compared, an enhanced minimax regret approach is proposed to compare and rank them. Finally, a real case study about online commodity evaluation is examined to show the implementation process of the proposed framework, and a discussion is also conducted to systematically analyze its superiority.

Synthetic damage effect assessment through evidential reasoning approach and neural fuzzy inference: Application in ship target

The damage effect assessment of anti-ship missiles combines system science and weapon science, which can provide reference for the assessment of battlefield damage situation. In order to solve the difficulty of heterogeneous data aggregation and the difficulty in constructing the mapping between factors and damage effect, this paper analyzes the specific damage process of the anti-ship missile to the ship, and proposes a synthetic Evidential Reasoning (ER) – Adaptive Neural Fuzzy Inference System (ANFIS) to assess the damage effect. To solve the problem of fuzziness and uncertainty of criteria in the assessment process, the belief structure model is used to transform qualitative and quantitative information into a unified mathematical structure, and ER algorithm is used to fuse the information of lower-level criteria. In order to solve the problem of fuzziness and uncertainty of the information contained in the first-level variables, and the strong non-linear characteristics of the mapping between first-level variables and damage effect, the ANFIS with self-adaptation and self-learning is constructed. The map between the three first-level variables and damage effect is established, and the interaction process of the various factors in the damage effect assessment are clear. Sensitivity analysis shows that assessment model has good stability. The result analysis and comparative analysis show that the process proposed in this paper can effectively assess the damage effect of anti-ship missiles, and all criteria data are objective and comparable.

Evidential reasoning based ensemble classifier for uncertain imbalanced data

Various studies have focused on the classification of uncertain or imbalanced data. However, previous studies rarely consider the classification for uncertain imbalanced data. To address this research gap, this study proposes an evidential reasoning (ER) based ensemble classifier (EREC). In the proposed method, an affinity propagation based oversampling method is developed to obtain the balanced class distributions of the training datasets for individual classifiers. Using the balanced training datasets, ER-based classifiers are constructed as individual classifiers to handle data uncertainty, in which attribute weights are learned from the similarity between the values of attributes and labels. With trained individual classifiers, final results are generated by combining the results of individual classifiers using the ER algorithm, in which the weights of individual classifiers are determined according to the classification performance on out-of-bag data. The proposed EREC is applied to the diagnosis of thyroid nodules using the datasets of five radiologists, obtained from a tertiary hospital located in Hefei, Anhui, China. Using real datasets and UCI datasets, the EREC is compared with 12 representative ensemble classifiers and other oversampling methods based ensemble classifiers to highlight its high performance.

Performance measurement of sustainable freight transportation: a consensus model and FERA approach

Sustainable freight transportation is aimed at reducing environmental emissions and social inequity along with economic inefficiency to drive the business. Lack of a continuous sustainability assessment and monitoring tool increases the reluctance of freight transporters to adopt world-class sustainability practices. This paper attempts to develop the performance index for a sustainable freight transportation system by innovatively integrating the Consensus Model (CM) with the Fuzzy Evidential Reasoning Algorithm (FERA). A CM has been used to determine the degree of importance of each Key Performance Indicators identified across three dimensions of sustainability. FERA has been used to aggregate subjective judgments with crisp quantitative values. This approach has a unique capability to handle various uncertainties related to impreciseness in decision-making. This study has demonstrated the use of an integrated approach for developing a performance index of the freight transportation system. Sensitivity Analysis of the model provides logical inferences and an understanding of the robustness of the model outputs. It can be interpreted from the results of the study that the firms with higher profitability in the market are focusing more on making their operations sustainable. A proportionate approach of firms towards the economic, environment, and societal well-being, may assist in achieving higher sustainability.

A Novel Weapon System Effectiveness Assessment Method Based on the Interval-Valued Evidential Reasoning Algorithm and the Analytical Hierarchy Process

As one of the most essential parts of the military operation, the ability of weapon systems to achieve the operation mission is vital to the success of a military operation. Focusing on the problem of weapon system effectiveness assessment (WSEA) under interval uncertainty, this paper proposed WSEA method that utilized the interval-valued evidential reasoning (ER) algorithm, the analytical hierarchy process (AHP), and the two-grade interval ranking method to properly deal with interval uncertainty in the assessment as well as provide reliable assessment results. Firstly, the AHP is used to determine the weight of different attributes of the weapon system based on experts’ knowledge, which could enhance the subjectiveness of the assessment result. Then, the interval-valued ER algorithm is applied to combine the assessment of different attributes under interval uncertainty and provide the overall assessment result. Finally, the two-grade interval ranking method is utilized to compare and rank the assessment results of different weapons. Case study shows that the proposed method could provide reliable and accurate results for weapon effectiveness assessment. Comparison results and sensitivity analysis results further confirm that the proposed method is not only as effective as existing methods with precise data, but also has the ability to provide reliable results under uncertainty. In conclusion, by utilizing the interval-valued ER algorithm, the AHP and the two-grade interval ranking method, the proposed method provides a novel and effective way for weapon effectiveness assessment under uncertainty, especially interval uncertainty.

Integrated Transformer Health Estimation Methodology Based on Markov Chains and Evidential Reasoning

Due to the large number of power transformers (ETs) in the distribution system, there is a need for a relatively simple representation of the status of each unit in order to more easily determine where and how to allocate the budget for preventive and corrective maintenance. In recent years, the concept of the transformer health index (HI) as an integral part of resource management was adopted for the condition assessment and ranking of ETs. HI algorithms take different forms and can be determined based on a large number of specific parameters. However, the main problem in HI methodology or any modern diagnostic technique is the existence of regular measurements and inspections and accurate test results. The paper proposes a solution in the form of the upgraded HI and the novel methodology for ET ranking including the value of available information to describe ET current state. The confidence to the measurement results is calculated using evidential reasoning (ER) algorithm based on Dempster–Shafer theory. The contribution to the ER methodology is the calculation of the initial degrees of belief using Markov chains. The aging process of an ET and transition probabilities from state to state are modelled using the statistical data for the population of 300 ETs and 20 years monitoring data. The proposed methodology is tested on the real data for 110/35 kV transformer, and in the second case, compared to the sample of 30 110/x kV transformers with traditional HI calculation.

A risk assessment model with systematical uncertainty treatment for container shipping operations

ABSTRACT The substantial adverse effects of risk factors on container shipping and logistics promoted a deep integration of risk analysis into the decision-making process. This paper aims to develop a well-grounded quantitative model to operational risk in a container shipping context. Considering uncertainty as a primary component of the risk concept, methods were employed in an inter-complementary manner to enable not only a sense of foreseeability but also a deeper look into the weaknesses of the knowledge base. The intersubjectivity of the input extraction process was supported by the Evidential Reasoning (ER) algorithm. Risks are then assessed based on a Fuzzy Rules Bayesian Network (FRBN) model with a 2-level parameter structure before meaningful interpretations can be derived through a new risk mapping approach. Besides an illustrative case study, the model was tested by sensitivity analysis and an examination of multiple validity claims.

Fault Tree and Fuzzy D-S Evidential Reasoning combined approach: An application in railway dangerous goods transportation system accident analysis

In order to manage the railway dangerous goods transportation system (RDNGTS) successfully, an explicit and effective previous accident analysis and accident control approach is essential and necessary. In this study a Fault Tree and Fuzzy D-S Evidential Reasoning combined approach is proposed to analyze the RDNGTS accident, which can solve the uncertainty modeling and information fusion problems existing in RDNGTS accident analysis. The approach has six steps: (i) identify causes of accident and calculate their weights based on Fault Tree, (ii) establish the Fuzzy Belief Structure model of causes of accident, (iii) handle initial qualitative and quantitative data, (iv) fuse the pre-processing data based on Fuzzy D-S Evidential Reasoning algorithm, (v) allocate Confidence Level of fuzzy intersection and, (vi) rank the final Fuzzy Belief Structure of each component based on trapezoidal fuzzy numbers and triangular fuzzy numbers. A historical lithium battery railway transportation accident happened in 2016 in China is applied as the background to examine the approach mentioned in this paper. The results show that professional skills and attitudes of transportation staffs are the weakest component in this lithium battery railway transportation accident. Managers of RDNGTS in China should pay more attentions to the professional skills and attitudes of transportation staffs. Some measures such as improve the awareness of safety and protection, train and examine the professional skills of transportation staffs, may be helpful in curbing the negligent working attitude of transportation staffs. Furthermore, the results also show that D-S Evidential Reasoning could provide a unified modeling framework for uncertain, incomplete, inaccurate and even ignorant information. It could solve the limitations in probability reasoning processes effectively.

Dependence Assessment in Human Reliability Analysis Based on the Interval Evidential Reasoning Algorithm Under Interval Uncertainty

Dependence assessment, which is to assess the influence of the operator’s failure of a task on the failure probability of subsequent tasks, is an important part in Human reliability analysis (HRA). The technique for human error rate prediction (THERP) has been widely applied to assess the dependence in HRA. However, due to the complexity of the real world, various kinds of uncertainty could occur in dependence assessment problem, and how to properly express and deal with uncertainty especially interval uncertainty remains a pressing issue. In this article, a novel method based on the interval evidential reasoning (IER) algorithm is proposed to assess dependence in HRA under interval uncertainty. First, dependence influential factors are identified and their functional relationship is determined. Then, judgments on these factors provided by the analysts are represented using interval belief distributions. Next, the interval evidential reasoning algorithm is employed to aggregate interval belief distributions of different factors according to their functional relationship while considering the credibility of the interval belief distribution. Finally, the conditional human error probability (CHEP) is calculated based on the fused interval belief distribution, where the upper and lower values are determined by assigning belief degree to the highest and lowest grade of the corresponding grade interval, respectively. Two numerical examples demonstrate that the proposed method not only properly deals with interval uncertainty using interval belief distribution and IER algorithm, but also provides a novel and effective way for dependence assessment in HRA.