Jian-Bo Yang

The University of Manchester, Manchester, England, United Kingdom

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Publications (110)74.88 Total impact

  • Jian-Bo Yang, Dong-Ling Xu
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    ABSTRACT: Analysing performances for future improvement and resource planning is a key management function. Data Envelopment Analysis (DEA) provides an analytical mean for performance modelling without assuming parametric functions. Multiple Objective Optimisation (MOO) is well-suited for resource planning. This paper reports an investigation in exploring relationships between DEA and MOO models for equivalent efficiency analysis in a MOO process. It is shown that under certain conditions minimax reference point models are identical to input-oriented dual DEA models for performance assessment. The former can thus be used for Hybrid Efficiency and Trade-off Analyses (HETA). In this paper, these conditions are first established and the equivalent models are explored both analytically and graphically to better understand HETA. Further investigation in the equivalence models leads to the modification of efficiency measures and the development of a minimax reference point approach for supporting integrated performance analysis and resource planning, with the Decision Maker’s (DM) preferences taken into account in an interactive fashion. Both numerical and case studies are conducted to demonstrate the proposed approach and its potential applications.
    Computers & Operations Research 01/2014; 46:78–90. · 1.91 Impact Factor
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    ABSTRACT: Expertons and uncertain aggregation operators are tools for dealing with imprecise information that can be assessed with interval numbers. This paper introduces the uncertain generalized probabilistic weighted averaging (UGPWA) operator. It is an aggregation operator that unifies the probability and the weighted average in the same formulation considering the degree of importance that each concept has in the aggregation. Moreover, it is able to assess uncertain environments that cannot be assessed with exact numbers but it is possible to use interval numbers. Thus, we can analyze imprecise information considering the minimum and the maximum result that may occur. Further extensions to this approach are presented including the quasi-arithmetic uncertain probabilistic weighted averaging operator and the uncertain generalized probabilistic weighted moving average. We analyze the applicability of this new approach in a group decision making problem by using the theory of expertons in strategic management.
    European Journal of Operational Research 01/2014; 235(1):215–224. · 2.04 Impact Factor
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    ABSTRACT: In this paper, a Genetic Algorithm (GA) search heuristic is proposed for validating the model-structure of Belief Rule-Based (BRB) methodologies. In order to ensure the balance between the model fit/ accuracy and the model complexity, the Akaike Information Criterion (AIC) is used in conjunction with the mentioned heuristic. The resulting framework is tested, using a model consisting of 3 inputs and one output, each of the 4 variables being allocated up to 5 referential values. The presented results illustrate the time-efficiency of the GA heuristic, as well as the penalty imposed by AIC on the number of parameters. The simplest model structure is indicated by AIC to be the optimal one. However, three additional model structures have been found to have AIC values which are moderately close to this optimum. An analysis of their coefficients of determination indicates a higher fit (than AIC optimum) on both testing sets and overall.
    Proceedings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics; 10/2013
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    ABSTRACT: The supply represents the available number of products of a specific variable. This paper presents a new approach for studying the supply through the use of aggregation systems. Its main advantage is that it permits to represent the information in a more complete way. Thus, it is possible to develop forecasting methods that consider optimistic and pessimistic scenarios and those that are more expected to occur. A wide range of aggregation operators are used giving special focus on the ordered weighted average (OWA). Several generalizations that use it with the weighted average and the probability are presented including the OWA weighted average (OWAWA) and the probabilistic OWAWA (POWAWA) operator. A numerical example is also presented.
    Proceedings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics; 10/2013
  • Yu-Wang Chen, Jian-Bo Yang, Dong-Ling Xu
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    ABSTRACT: Belief rule-based (BRB) systems are an extension of traditional IF-THEN rule based systems and capable of capturing complicated nonlinear causal relationships between antecedent attributes and consequents. In a BRB system, various types of information and knowledge with uncertainties can be represented using belief structures, and a belief rule is designed with belief degrees embedded in its possible consequents. In this paper, we first review the scheme of belief rules for representing and inferring uncertain knowledge. Then we present two BRB system identification methods in which different training objectives are used. Finally, numerical studies are conducted to demonstrate the capability of BRB systems on uncertain nonlinear system modeling and identification.
    Proceedings of the 2013 international conference on Integrated Uncertainty in Knowledge Modelling and Decision Making; 07/2013
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    ABSTRACT: Belief rule based (BRB) system provides a generic inference framework for approximating complicated nonlinear causal relationships between antecedent inputs and output. It has been successfully applied to a wide range of areas, such as fault diagnosis, system identification and decision analysis. In this paper, we provide analytical and theoretical analyses on the inference and approximation properties of BRB systems. We first investigate the unified multi-model decomposition structure of BRB systems, under which the input space is partitioned into different local regions. Then we analyse the distributed approximation process of BRB systems. These analysis results unveil the underlying inference mechanisms that enable BRB systems to have superior approximation performances. Furthermore, by using the Stone–Weierstrass theorem, we constructively prove that BRB systems can approximate any continuous function on a compact set with arbitrary accuracy. This result provides a theoretical foundation for using and training BRB systems in practical applications. Finally, a numerical simulation study on the well-known benchmark nonlinear system identification problem of Box–Jenkins gas furnace is conducted to illustrate the validity of a BRB system and show its inference and approximation capability.
    Information Sciences. 06/2013; 234:121–135.
  • Mi Zhou, Xin-Bao Liu, Jian-Bo Yang, Chang Fang
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    ABSTRACT: Multiple attribute decision analysis (MADA) problems often include both qualitative and quantitative attributes which may be either precise or inaccurate. The evidential reasoning (ER) approach is one of reliable and rational methods for dealing with MADA problems and can generate aggregated assessments from a variety of attributes. In many real world decision situations, accurate assessments are difficult to provide such as in group decision situations. Extensive research in dealing with imprecise or uncertain belief structures has been conducted on the basis of the ER approach, such as interval belief degrees, interval weights and interval uncertainty. In this paper, the weights of attributes and utilities of evaluation grades are considered to be fuzzy numbers for the ER approach. Fuzzy analytic hierarchy process (FAHP) is used for generating triangular fuzzy weights for attributes from a triangular fuzzy judgment matrix provided by an expert. The weighted arithmetic mean method is proposed to aggregate the triangular fuzzy weights of attributes from a group of experts. α-cut is then used to transform the combined triangular fuzzy weights to interval weights for the purpose of dealing with the fuzzy type of weight and utility in a consistent way. Several pairs of group evidential reasoning based nonlinear programming models are then designed to calculate the global fuzzy belief degrees and the overall expected interval utilities of each alternative with interval weights and interval utilities as constraints. A case study is conducted to show the validity and effectiveness of the proposed approach and sensitivity analysis is also conducted on interval weights generated by different α-cuts.
    International Journal of Computational Intelligence Systems 03/2013; 6(3):423-441. · 1.47 Impact Factor
  • Jian-Bo Yang, Dong-Ling Xu
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    ABSTRACT: This paper aims to establish a unique Evidential Reasoning (ER) rule to combine multiple pieces of independent evidence conjunctively with weights and reliabilities. The novel concept of Weighted Belief Distribution (WBD) is proposed and extended to WBD with Reliability (WBDR) to characterise evidence in complement of Belief Distribution (BD) introduced in Dempster–Shafer (D–S) theory of evidence. The implementation of the orthogonal sum operation on WBDs and WBDRs leads to the establishment of the new ER rule. The most important property of the new ER rule is that it constitutes a generic conjunctive probabilistic reasoning process, or a generalised Bayesian inference process. It is shown that the original ER algorithm is a special case of the ER rule when the reliability of evidence is equal to its weight and the weights of all pieces of evidence are normalised. It is proven that Dempsterʼs rule is also a special case of the ER rule when each piece of evidence is fully reliable. The ER rule completes and enhances Dempsterʼs rule by identifying how to combine pieces of fully reliable evidence that are highly or completely conflicting through a new reliability perturbation analysis. The main properties of the ER rule are explored to facilitate its applications. Several existing rules are discussed and compared with the ER rule. Numerical and simulation studies are conducted to show the features of the ER rule.
    Artificial Intelligence. 01/2013; 205:1–29.
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    ABSTRACT: Lymph Node Metastasis (LNM) in gastric cancer is an important prognostic factor regarding long-term survival. As it is difficult for doctors to combine multiple factors for a comprehensive analysis, Clinical Decision Support System (CDSS) is desired to help the analysis. In this paper, a novel Bi-level Belief Rule Based (BBRB) prototype CDSS is proposed. The CDSS consists of a two-layer Belief Rule Base (BRB) system. It can be used to handle uncertainty in both clinical data and specific domain knowledge. Initial BRBs are constructed by domain specific knowledge, which may not be accurate. Traditional methods for optimizing BRB are sensitive to initialization and are limited by their weak local searching abilities. In this paper, a new Clonal Selection Algorithm (CSA) is proposed to train a BRB system. Based on CSA, efficient global search can be achieved by reproducing individuals and selecting their improved maturated progenies after the affinity maturation process. The proposed prototype CDSS is validated using a set of real patient data and performs extremely well. In particular, BBRB is capable of providing more reliable and informative diagnosis than a single-layer BRB system in the case study. Compared with conventional optimization method, the new CSA could improve the diagnostic performance further by trying to avoid immature convergence to local optima.
    Knowledge-Based Systems. 01/2013; 54:128–136.
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    ABSTRACT: A belief rule-based (BRB) system is a generic nonlinear modelling and inference scheme. It is based on the concept of belief structures and evidential reasoning (ER), and has been shown to be capable of capturing complicated nonlinear causal relationships between antecedent attributes and consequents. The aim of this paper is to develop a BRB system that complements the RiskMetrics WealthBench system for portfolio optimisation with nonlinear cash-flows and constraints. Two optimisation methods are presented to locate efficient portfolios under different constraints specified by the investors. Numerical studies demonstrate the effectiveness and efficiency of the proposed methodology.
    European Journal of Operational Research 12/2012; 223(3):775–784. · 2.04 Impact Factor
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    ABSTRACT: It is widely acknowledged that understanding and prioritizing the voice of customer is a critical step in new product development. In this work, we propose a novel approach to handle missing and incomplete data while combining information from different surveys for prioritizing customer voices. Our new approach comprises of the following stages: estimating and representing missing and incomplete data; estimating intervals for the criteria used in analyzing data; mapping data on criteria to a common scale; modeling interval data using interval belief structure; and aggregating evidence and ranking customer voices using the interval evidential reasoning algorithm. We demonstrate our approach using a case study from automotive domain with a given criteria hierarchy for analyzing data from three different surveys. We propose new optimization formulations for estimating intervals of the criteria used in our case study and logical yet pragmatic transformation functions for mapping criteria values to a common scale.
    European Journal of Operational Research 08/2012; 220(3):762–776. · 2.04 Impact Factor
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    ABSTRACT: This paper describes a prototype clinical decision support system (CDSS) for risk stratification of patients with cardiac chest pain. A newly developed belief rule-based inference methodology-RIMER was employed for developing the prototype. Based on the belief rule-based inference methodology, the prototype CDSS can deal with uncertainties in both clinical domain knowledge and clinical data. Moreover, the prototype can automatically update its knowledge base via a belief rule base (BRB) learning module which can adjust BRB through accumulated historical clinical cases. The domain specific knowledge used to construct the knowledge base of the prototype was learned from real patient data. We simulated a set of 1000 patients in cardiac chest pain to validate the prototype. The belief rule-based prototype CDSS has been found to perform extremely well. Firstly, the system can provide more reliable and informative diagnosis recommendations than manual diagnosis using traditional rules when there are clinical uncertainties. Secondly, the diagnostic performance of the system can be significantly improved after training the BRB through accumulated clinical cases.
    Fuel and Energy Abstracts 06/2012;
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    ABSTRACT: The health system in developed countries is facing a problem of scalability in order to accommodate the increased proportion of the elderly population. Scarce resources cannot be sustained unless innovative technology is considered to provide health care in a more effective way. The Smart Home provides preventive and assistive technology to vulnerable sec-tors of the population. Much research and development has been focused on the technological side (e.g., sensors and networks) but less effort has been invested in the capability of the Smart Home to intelligently monitor situations of interest and act in the best interest of the occupants. In this article we model a Smart Home scenario, using knowledge in the form of Event-Condition-Action rules together with a new inference scheme which in-corporates spatio-temporal reasoning and uncertainty. A reasoning system called RIMER, has been extended to permit the monitoring of situations according to the place where they occur and the specific order and duration of the activities. The system allows for the specification of uncertainty both in terms of knowledge rep-resentation and credibility of the conclusions that can be achieved in terms of the evidence available.
    03/2012;
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    ABSTRACT: In multi-attribute group decision making (MAGDM) problems, decision attributes of alternatives are often considered as with quantitative and qualitative information. Therefore decision making problems may include preference information in different formats. In this paper, a multi-attribute group decision making model based on numerical and uncertain linguistic information is investigated. Uncertain linguistic information reflects the subjectivity and uncertainty of evaluation with respect to qualitative attributes. A transformation function and an extended TOPSIS procedure are proposed to deal with this MAGDM model with numerical and uncertain linguistic information. In order to implement the TOPSIS procedure, the distance of interval numbers and the distance of n-dimensional interval numbers are defined, they are both in form of interval numbers to preserve the uncertainty of original information. The proposed approach is illustrated by a numerical example, and is applied in the evaluation of R&D projects in the final part of the paper.
    International Journal of Computational Intelligence Systems. 03/2012; 3(5).
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    ABSTRACT: Rapid and accurate identification of consumer demands and systematic assessment of product quality are essential to success for new product development, in particular for fast moving consumer goods such as food and drink products. This paper reports an investigation into a belief rule-based (BRB) methodology for quality assessment, target setting and consumer preference prediction in retro-fit design of food and drink products. The BRB methodology can be used to represent the relationships between consumer preferences and product attributes, which are complicated and nonlinear. A BRB system can initially be established using expert knowledge and then optimally trained and validated using data generated from consumer or expert panel assessments or from tests and experiments. The established BRBs can then be used to predict the consumer acceptance of new products or set product target values in retro-fit design. The proposed BRB methodology is applied to the design of a lemonade drink product using real data provided by a sensory product manufacturer in the UK. The results show that the BRB methodology can be used to predict consumer preferences with high accuracy and to set optimal target values for product quality improvement.
    Expert Syst. Appl. 01/2012; 39:4749-4759.
  • Mi Zhou, Xin-Bao Liu, Jian-Bo Yang
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    ABSTRACT: In multiple attribute decision making problems, the variety of attribute becomes large because of the complex decision environment and uncertainties in the process of decision making. In the existent evidential reasoning approach, only benefit and cost attribute have been considered, so it could not deal with the decision problem with other kinds of attribute. In this paper, deviating attribute is added to the ER based MADM problems. Specifically, the representation of deviating attribute by evidence is analyzed, and the transformation rules from deviating attribute assessment values to belief degrees are studied. As the result, the applications of ER based assessment approach are enlarged.
    01/2012;
  • Jian-Bo Yang, Dong-Ling Xu, Shanlin Yang
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    ABSTRACT: A DEA-oriented Interactive Minimax Reference Point (DEA-IMRP) approach was recently developed to support integrated performance assessment and target setting for consistent management control and planning. To conduct the Integrated Efficiency and Trade-off (IET) analyses using the DEA-IMRP approach, it is important to understand the characteristics of the efficiency frontier and interactive trade-off analysis process. In this paper, the features of the IET analyses are investigated in detail. Graphical and analytical methods and procedures are explored for generating and analysing data envelopes and efficient frontiers for multiple input and multiple output DEA models using the DEA-IMRP approach. This computational investigation generates useful insights into the IET analyses and leads to the definition of new efficiency measures, which are instrumental to help conduct trade-off analysis for setting realistic performance targets. A numerical example is studied to illustrate the findings graphically. A case study for UK retail banks is conducted using the new methods and procedures investigated in this paper.
    Computers & OR. 01/2012; 39:1062-1073.
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    ABSTRACT: In order to determine the parameters of belief-rule-base (BRB) accurately, several optimization methods have been proposed for training BRB, on the basis of a generic rule-base inference methodology using the evidential reasoning (RIMER) approach. These optimization methods are implemented offline, and such are not suitable for training BRB in a dynamic fashion. In this paper, two recursive algorithms are proposed to update BRB online that can simulate dynamic systems. The main feature of the proposed algorithms is that only partial input and output information is required, which can be incomplete or vague, numerical or judgmental, or mixed. If the internal structure of a BRB is initially decided using expert judgments, domain-specific knowledge and/or commonsense rules, the proposed algorithms can be used to fine-tune the initial BRB online, once input and output datasets become available. Using the proposed algorithms, there is no need to collect a complete set of data before a BRB can be trained, which is necessary if the BRB is used to simulate a dynamic system. A numerical example and a case study are reported to demonstrate the potential of the algorithms for online fault diagnosis.
    IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans 12/2011; · 2.18 Impact Factor
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    ABSTRACT: Recently, a novel prediction model based on the evidential reasoning (ER) approach is developed to forecast reliability in engineering systems. In order to determine the parameters of the ER-based predic- tion model, some optimization models have been proposed to train the ER-based prediction model. However, these models are implemented in an offline fashion and thus it is very expensive to train and retrain them when new information is available. This correspondence paper is concerned with developing the recursive algorithms for updating the ER-based prediction model from the probability-based point of view. Using the recursive ex- pectation maximization algorithm, two recursive algorithms are proposed for updating the parameters of the ER-based prediction model under judgmental and numerical outputs, respectively. As such, the proposed algorithms can be used to fine tune the ER-based prediction model online once new information becomes available. We verify the proposed method via a realistic example with missile reliability data. Index Terms—Decision analysis, expectation maximization (EM), forecasting, recursive algorithms, uncertainty.
    IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans 01/2011; 41:1268-1277. · 2.18 Impact Factor
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    ABSTRACT: Belief rule base (BRB) systems are an extension of traditional IF-THEN rule based systems and capable of capturing complicated nonlinear causal relationships between antecedent attributes and consequents. In a BRB system, various types of information with uncertainties can be represented using belief structures, and a belief rule is designed with belief degrees embedded in its possible consequents. For a set of inputs to antecedent attributes, inference in BRB is implemented using the evidential reasoning (ER) approach. In this paper, the inference mechanism of the ER algorithm is analyzed first and its patterns of monotonic inference and nonlinear approximation are revealed. For a practical BRB system, it is difficult to determine its parameters accurately by using only experts’ subjective knowledge. Moreover, the appropriate adjustment of the parameters of a BRB system using available historical data can lead to significant improvement on its prediction performance. In this paper, a training data selection scheme and an adaptive training method are developed for updating BRB parameters. Finally, numerical studies on a multi-modal function and a practical pipeline leak detection problem are conducted to illustrate the functionality of BRB systems and validate the performance of the adaptive training technique.
    Expert Syst. Appl. 01/2011; 38:12845-12860.

Publication Stats

2k Citations
74.88 Total Impact Points

Institutions

  • 2000–2014
    • The University of Manchester
      • • Manchester Business School (MBS)
      • • Decision and Cognitive Sciences Research Centre
      Manchester, England, United Kingdom
  • 2011
    • Hefei University of Technology
      • School of Management
      Luchow, Anhui Sheng, China
    • Xi'an Institute of Technology
      Ch’ang-an, Shaanxi, China
  • 2009
    • University of Cape Town
      • Department of Statistical Sciences
      Kaapstad, Western Cape, South Africa
  • 2007–2009
    • Huazhong University of Science and Technology
      • Institute of Systems Engineering
      Wu-han-shih, Hubei, China
    • Universidad de Jaén
      • Department of Computer Sciences
      Jaén, Andalusia, Spain
  • 2005–2007
    • Fuzhou University
      Min-hou, Fujian, China
  • 2006
    • Shanghai Jiao Tong University
      Shanghai, Shanghai Shi, China