D-CFPR: D numbers extended consistent fuzzy preference relations

03/23/2014 ∙ by Xinyang Deng, et al. ∙ Southwest University 0

How to express an expert's or a decision maker's preference for alternatives is an open issue. Consistent fuzzy preference relation (CFPR) is with big advantages to handle this problem due to it can be construed via a smaller number of pairwise comparisons and satisfies additive transitivity property. However, the CFPR is incapable of dealing with the cases involving uncertain and incomplete information. In this paper, a D numbers extended consistent fuzzy preference relation (D-CFPR) is proposed to overcome the weakness. The D-CFPR extends the classical CFPR by using a new model of expressing uncertain information called D numbers. The D-CFPR inherits the merits of classical CFPR and can be totally reduced to the classical CFPR. This study can be integrated into our previous study about D-AHP (D numbers extended AHP) model to provide a systematic solution for multi-criteria decision making (MCDM).

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1 Introduction

Preference relation has played a fundamental role in most decision processes Xu2007surverypr . According to previous studies, the preference relation can be divided into two categories. The first one is multiplicative preference relation herrera2001multiperson ; chiclana2004induced ; liu2012goal ; xia2014multiplicative which is subjected to the multiplicative reciprocal, i.e. . The second one is fuzzy preference relation Tanino1984fuzzypref ; gong2008least ; parreiras2012dynamic ; rezaei2013multi which is described by fuzzy pairwise comparison with an additive reciprocal, i.e. . As the basic element of many decision making methods especially in analytic hierarchy process (AHP) model saaty1980analytic ; chan2007global ; ishizaka2011selection ; ishizaka2013calibrated , the preference relation has attracted many interests fernandez2010solving ; evren2011multi ; xu2012error ; liu2012least ; xu2013distance ; xia2013preference ; Wang2014169 .

Fuzzy preference relations herrera2007consensus ; liu2012new provide a method to construct the decision matrices of pairwise comparisons based on the linguistic values given by experts. The value given by the experts represents the degree of the preference for the first alternative over the second alternative. Assume there are alternatives, a total of pairwise comparisons need to be answered for constructing a fuzzy preference relation. What’s more, there always exists a potential risk that the constructed fuzzy preference relation is inconsistent due to the inability of human beings to deal with overcomplicated objects chiclana2002note ; xu2013ordinal ; xia2013algorithms . In order to overcome the deficiencies, Herrera-Viedma et al. HerreraViedma2004someiss proposed consistent fuzzy preference relation (CFPR) to construct the pairwise comparison decision matrices based on additive transitivity property Tanino1984fuzzypref ; tanino1988fuzzy , i.e. . The merit of CFPR consists of two aspects. Firstly, only is a total of pairwise comparisons needed to construct a CFPR. Secondly, it is always consistent in a CFPR. Due to these merits, the CFPR is widely used in many fields wang2007applying ; chen2012supplier ; lan2012deriving .

Although the CFPR is so advantageous to express experts’ or decision makers’ preferences, however, the original CFPR is constructed on the foundation of complete and certain information. It is unable to deal with the cases involving incomplete and uncertain information. For example, an expert gives that the first alternative is more important than the second alternative . How to express the linguistic variable “more important”? Some one would say it means , some others will say it seems that . Or it is more reasonable that with a degree and with a degree , where . Besides, incomplete information means the preference values are not complete due to the lack of knowledge and the limitation of cognition. For example, an expert gives with a belief of 0.7, the remainder 0.3 belief can not be assigned to any linguistic values due to the lack of knowledge. With respect to these cases involving uncertain and incomplete entries, the conventional CFPR is incapable.

To overcome these weaknesses, D numbers Deng2012DNumbers ; Deng2014DAHPSupplier ; Deng2014EnvironmentDNs ; Deng2014BridgeDNs , a new model of expressing uncertain information, has been employed in the construction of CFPR. A new preference relation called D numbers extended consistent fuzzy preference relation (short for D-CFPR) is proposed in this paper. The D-CFPR uses D numbers to express the linguistic preference values given by experts or decision makers, and it can be reduced to classical CFPR. In contrast with the construction of CFPR, a method is proposed for constructing the D-CFPR, and the proposed method is also effective to construct the CFPR. In addition, the priority weights and ranking of alternatives can be obtained from a D-CFPR based on our previous work Deng2014DAHPSupplier . In Deng2014DAHPSupplier , we proposed a D-AHP (D numbers extended AHP) model for multi-criteria decision making (MCDM). The work in this paper provides a solution of constructing the preference matrix for D-AHP. Based on these two studies, we systematically provide a novel solution for MCDM problems.

The rest of this paper is organized as follows. Section 2 gives a brief introduction about the consistent fuzzy preference relation. In Section 3, the proposed D numbers extended consistent fuzzy preference relation is presented. Some numerical examples are given in Section 4. Finally, concluding remarks are given in Section 5.

2 Consistent fuzzy preference relations (CFPR)

Fuzzy preference relations Xu2007surverypr ; Tanino1984fuzzypref ; herrera2007consensus ; HerreraViedma2004someiss enable an expert or a decision maker to give linguistic values for the comparison of alternatives or creteria. The preference values employed in a fuzzy preference relation are real numbers belonging to . A reciprocal fuzzy preference relation on a set of alternatives is represented by a fuzzy set on the product set , and is characterized by a membership function Xu2007surverypr ; herrera2007consensus ; HerreraViedma2004someiss

(1)

when the cardinality of is small, the preference relation may be conveniently represented by an matrix , being , namely

(2)

where (1) ; (2) ; (3) . denotes the preference degree of alternative over alternative .

Herrera-Viedma et al. HerreraViedma2004someiss proposed the consistent fuzzy preference relation (CFPR) for the construction of pairwise comparison decision matrices based on additive transitivity property Tanino1984fuzzypref ; tanino1988fuzzy . A reciprocal fuzzy preference relation is called a consistent fuzzy preference relation if and only if . For a CFPR , the following two equations are satisfied HerreraViedma2004someiss :

(3)
(4)

The biggest advantage of CFPRs is that it is reciprocal and consistent. Based on the results presented in Eq.(3) and Eq.(4), a CFPR can be constructed from the set of values . That means only pairwise comparisons are required in the process of constructing a CFPR. Compared with the construction of the ordinary fuzzy preference relation which requires pairwise comparisons, the rest of pairwise comparisons are computed by using additive transitivity property in the construction of CFPRs. It is noted that the values in the generated CFPR may do not fall in the interval , but fall in an interval , . In such a case, the values in the obtained CFPR need to be transformed by using a transformation function that preserves reciprocity and additive consistency. The transformation function is defined as HerreraViedma2004someiss :

(5)

3 Proposed D numbers extended consistent fuzzy preference relations (D-CFPR)

3.1 D numbers

D number Deng2012DNumbers ; Deng2014DAHPSupplier ; Deng2014EnvironmentDNs ; Deng2014BridgeDNs is a new model of representing uncertain information. It has extended the Dempster-Shafer theory Dempster1967 ; Shafer1976 . Dempster-Shafer theory is with advantages to handle uncertain information liu2006analyzing ; schubert2011conflict ; jousselme2012distances ; yang2013evidential ; Deng2014PAontheaxi ; dubois2012conditioning ; lefevre2013preserve ; dezert2014validity , and is extensively used in many fields, such as risk assessment zhang2012assessment ; bolar2013condition , expert systems yang2006belief ; zhou2013bi , classification and clustering denoeux2004evclus ; bi2008combination

, parameter estimation

denoeux2013maximum , decision making casanovas2012fuzzy ; mokhtari2012decision ; yager2013decision , and so forth srivastava2009representation ; klein2012belief ; cuzzolin2010geometry ; cuzzolin2012relative ; kang2012evidential ; wei2013identifying ; Deng2013TOPPER . However, there are some weaknesses in Dempster-Shafer theory. D numbers overcome a few of existing deficiencies (i.e., exclusiveness hypothesis and completeness constraint) in Dempster-Shafer theory and appear to be more effective in representing various types of uncertainties. Some basic concepts about D numbers are given as follows.

Let be a nonempty set satisfying if , , a D number is a mapping formulated by

(6)

with

(7)

where is the empty set and is a subset of .

If , the information expressed by the D number is said to be complete; if , the information is said to be incomplete. The degree of information’s completeness in a D number is defined as below.

Let be a D number on a finite nonempty set , the degree of information’s completeness in is quantified by

(8)

For the sake of simplification, the degree of information’s completeness of a D number is called as its value.

For a discrete set , where and if , a special form of D numbers can be expressed by Deng2014DAHPSupplier ; Deng2014EnvironmentDNs

(9)

or simply denoted as , where if , and . Some properties of this form of D numbers are introduced as follows.

Permutation invariability. If there are two D numbers that and , then .

For a D number , the integration representation of is defined as

(10)

where , and . For the sake of simplification, the integration representation of a D number is called as its value.

3.2 D-CFPR: D numbers extended CFPR

As mentioned above, the CFPR provides an option to establish the decision matrix which only requires pairwise comparisons. Moreover, the reciprocity and additive consistency have been preserved in a CFPR. However, the original CFPR is constructed on the foundation of complete and certain information. It is unable to deal with the cases involving incomplete and uncertain information. This deficiency also has existed in the fuzzy preference relation. For example, assume there are experts who were invited to evaluate alternatives and . Consider these cases.

Case 1: experts evaluate that is preferred to with a degree , the remainder experts evaluate that is preferred to with a degree , where and .

Case 2: experts evaluate that is preferred to with a degree , the remainder experts do not give any evaluations due to the lack of knowledge, where and .

Obviously, both the original CFPR and fuzzy preference relation are incapable of representing and handling the aforementioned cases. In Deng2014DAHPSupplier we studies the deficiency in the situation of fuzzy preference relations, and proposed the concept of D numbers preference relations which extends the fuzzy preference relations by using D numbers in order to overcome this deficiency. In this paper, we concentrate on the deficiency in the situation of CFPRs. The D numbers extended CFPR, shorted for D-CFPR, is proposed to strengthen CFPR’s ability of expressing uncertain information by using D numbers. the D-CFPR is formulated by

(11)

where , , and , , . Obviously, in .

In Eq.(11), is called a D-CFPR because it is constructed based on pairwise comparisons denoted as which is a set of D numbers. Here, a method is proposed to implement the construction of D-CFPRs.

At first, for the elements in the set of in which , is given by

(12)

in which every component is obtained by

(13)
(14)

where , and is the th component of , is the th component of , , is the th component of .

At second, for the rest of entries in the D-CFPR, they can be calculated based on the reciprocal property, namely , .

According to the aforementioned two steps, a D-CFPR can be constructed based on pairwise comparisons . For the generated D-CFPR, it is possible that some values of s in these D numbers do not fall in the interval , but fall in an interval , . In such a case, the values of all s in every D numbers need to be transformed by using a transformation function which is given in Eq.(5). The transformation function works as normalization, which transforms the values of s from to the interval .

In summary, the proposed method to construct a D-CFPR on alternatives from preference values is implemented as the following steps:

  1. Start. Let as input.

  2. Calculate the set of preference values as

    ,

    .

  3. . For , if the values of all s in each D numbers fall in the interval , the D-CFPR is obtained as , go to step 6; otherwise, go to next step.

  4. , , .

  5. The D-CFPR is obtained as such that

    ,

    , , .

  6. End.

Up to now, the method to construct a D-CFPR is totally presented. It should be pointed that the D-CFPR will reduce to the classical CFPR if the D numbers based preference values are substituted by real numbers. And the method to construct a D-CFPR is completely suitable for the construction of CFPRs. The proposed D-CFPR is an extension of the classical CFPR.

3.3 Solution for the D-CFPR

Once a D-CFPR has been constructed, another key problem is aroused that how to obtain the ranking and priority weights of alternatives based on the D-CFPR. In Deng2014DAHPSupplier , we studied the solution for D numbers preference relations which extends the fuzzy preference relations by using D numbers. Differ from common D numbers preference relations, the D-CFPR is obtained based the additive transitivity property of CFPRs. The proposed D-CFPR in the paper essentially is an special case of D numbers preference relations. Therefore, the solution for D numbers preference relations is also suitable for D-CFPRs. The procedure of the solution for D-CFPRs is shown in Figure. 1. At first, the D-CFPR is converted to an values matrix by using Eq.(10

). At second, construct a probability matrix

based on the values matrix to represent the preference probability between pairwise alternatives. At third, a triangular probability matrix can be obtained in terms of probability matrix with the aid of local information which contains the preference relation of pairwise alternatives. According to , the ranking of alternatives is determined. At fourth, a triangulated values matrix is generated based on and , and the weights of alternatives can calculated through . Please refer to literature Deng2014DAHPSupplier for more details.

Figure 1: The procedure to obtain the ranking and priority weights of alternatives based on the D-CFPR Deng2014DAHPSupplier

3.4 Inconsistency for the D-CFPR

The classical CFPR is totally consistent due to it is constructed based on the additive transitivity property from preference values. As an extension of CFPRs, the D-CFPR is also totally consistent when it has reduced to the classical CFPR. When the D-CFPR is with entries which contain uncertain or incomplete information, it is not totally consistent. In order to measure the inconsistency of D-CFPRs, an inconsistency degree defined for D numbers preference relations Deng2014DAHPSupplier is utilized to express such inconsistency. The inconsistency degree is on the basis of the triangular probability matrix .

(15)

4 Numerical examples

In this section, some numerical examples are given to show the construction of D-CFPRs.

4.1 Example 1: preference values with certain information

This example is from literature HerreraViedma2004someiss . Suppose there is a set of four alternatives where we have certain knowledge to assure that alternative is weakly more important than alternative , alternative is more important than and finally alternative is strongly more important than alternative . Suppose that this situation is modelled by the preference values .

By applying the method proposed in HerreraViedma2004someiss , a CFPR is obtained:

(16)

Howver, if the preference values are seen as a set of D numbers , a D-CFPR can be obtained by using the proposed method in this paper as follows.

due to

,

.

due to

,

.

due to

,

.

, , , , , , and therefore:

(17)

For , the values of all s in each D numbers fall in the interval , so the final D-CFPR . D-CFPR is identical with CFPR shown in Eq.(16).

Finally, the method proposed in literature Deng2014DAHPSupplier is utilized in order to obtain the priority weight of each alternative. The calculating process is omitted. Some important intermediate results are given as follows.

(18)
(19)
(20)
(21)

The inconsistency degree is . The obtained Priority weights and ranking of alternatives are shown in Table 1.

Alternatives Priority(different credibility of preference values) Ranking
High Medium Low Interval range
0.338 0.294 0.272 (0.250, 0.409] 1
0.312 0.281 0.266 (0.250, 0.364] 2
0.237 0.244 0.247 [0.227, 0.250) 3
0.112 0.181 0.216 [0.000, 0.250) 4
Table 1: Priority weights and ranking of alternatives in Example 1

4.2 Example 2: preference values with uncertain and incomplete information

In this example, we make a change to the preference values from the above example so that the preference values are with uncertain and incomplete information. Assume this situation is modelled by a set of D numbers .

In this case, the preference values involve incomplete entry (i.e., ) and uncertain entry (i.e., ). The classical CFPR can not deal with this case, but the proposed D-CFPR is effective for this case. A D-CFPR can be obtained as follows.

due to

,

;

due to

,

;

,

;

due to

,

.

,

.

, , , , , , and therefore:

(22)

Due to the preference values in do not totally fall in the interval , but fall in an interval , the transformation function shown in Eq.(5) will be used to obtain the final D-CFPR . The result is given as below.

(23)

Similar with Example 1, in order to obtain the priority weight of each alternative, the method proposed in literature Deng2014DAHPSupplier is employed. Some important intermediate results are given as follows.

(24)
(25)
(26)
(27)

In this example, the inconsistency degree is . The obtained Priority weights and ranking of alternatives are shown in Table 2.

Alternatives Priority(different credibility of preference values) Ranking
High Medium Low Interval range
0.327 0.289 0.269 (0.250, 0.402] 1
0.309 0.280 0.265 (0.250, 0.366] 2
0.241 0.246 0.248 [0.232, 0.250) 3
0.123 0.186 0.218 [0.000, 0.250) 4
Table 2: Priority weights and ranking of alternatives in Example 2

5 Concluding remarks

In this paper, we have studied the consistent fuzzy preference relation (CFPR) by combining with D numbers. The proposed new preference relation is called D numbers extended consistent fuzzy preference relation, shorted for D-CFPR. A method is proposed to implement the construction of D-CFPR based on a set of preference values which are expressed by D numbers. In comparison with CFPR, D-CFPR is able to deal with the case that preference values involve incomplete and uncertain information. D-CFPR can be reduced to the classical CFPR when the preference values expressed by D numbers have degenerated to real numbers. What’s more, based on our previous study in Deng2014DAHPSupplier , the priority weights and ranking of alternatives can be obtained given a D-CFPR. In Deng2014DAHPSupplier , the hierarchical structure of D-AHP model has been established. In this paper, the proposed D-CFPR can be employed to construct the preference matrix for D-AHP. Based on these two studies, the D-AHP model has been systematically built. In the future, we will focus on the application of proposed D-CFPR and D-AHP model.

Acknowledgements

The work is partially supported by National Natural Science Foundation of China (Grant nos. 61174022 and 71271061), National High Technology Research and Development Program of China (863 Program) (Grant no. 2013AA013801), R & D Program of China (2012BAH07B01).

References

  • (1) Z. Xu, A survey of preference relations, International Journal of General Systems 36 (2) (2007) 179–203.
  • (2) F. Herrera, E. Herrera-Viedma, F. Chiclana, Multiperson decision-making based on multiplicative preference relations, European journal of operational research 129 (2) (2001) 372–385.
  • (3) F. Chiclana, E. Herrera-Viedma, F. Herrera, S. Alonso, Induced ordered weighted geometric operators and their use in the aggregation of multiplicative preference relations, International Journal of Intelligent Systems 19 (3) (2004) 233–255.
  • (4) F. Liu, W.-G. Zhang, Z.-X. Wang, A goal programming model for incomplete interval multiplicative preference relations and its application in group decision-making, European Journal of Operational Research 218 (3) (2012) 747–754.
  • (5) M. Xia, Z. Xu, Z. Wang, Multiplicative consistency-based decision support system for incomplete linguistic preference relations, International Journal of Systems Science 45 (3) (2014) 625–636.
  • (6) T. Tanino, Fuzzy preference orderings in group decision making, Fuzzy Sets and Systems 12 (12) (1984) 117–131.
  • (7) Z.-W. Gong, Least-square method to priority of the fuzzy preference relations with incomplete information, International Journal of Approximate Reasoning 47 (2) (2008) 258–264.
  • (8) R. Parreiras, P. Ekel, F. Bernardes Jr, A dynamic consensus scheme based on a nonreciprocal fuzzy preference relation modeling, Information Sciences 211 (2012) 1–17.
  • (9) J. Rezaei, R. Ortt, Multi-criteria supplier segmentation using a fuzzy preference relations based AHP, European Journal of Operational Research 225 (1) (2013) 75–84.
  • (10) T. L. Saaty, The analytic hierarchy process: Planning, priority setting, resources allocation, McGraw-Hill, New York, 1980.
  • (11) F. T. Chan, N. Kumar, Global supplier development considering risk factors using fuzzy extended AHP-based approach, Omega 35 (4) (2007) 417–431.
  • (12) A. Ishizaka, A. Labib, Selection of new production facilities with the group analytic hierarchy process ordering method, Expert Systems with Applications 38 (6) (2011) 7317–7325.
  • (13) A. Ishizaka, N. H. Nguyen, Calibrated fuzzy AHP for current bank account selection, Expert Systems with Applications 40 (9) (2013) 3775–3783.
  • (14) A. Fernández, M. Calderón, E. Barrenechea, H. Bustince, F. Herrera, Solving multi-class problems with linguistic fuzzy rule based classification systems based on pairwise learning and preference relations, Fuzzy sets and systems 161 (23) (2010) 3064–3080.
  • (15) Ö. Evren, E. A. Ok, On the multi-utility representation of preference relations, Journal of Mathematical Economics 47 (4) (2011) 554–563.
  • (16) Z. Xu, An error-analysis-based method for the priority of an intuitionistic preference relation in decision making, Knowledge-Based Systems 33 (2012) 173–179.
  • (17) X. Liu, Y. Pan, Y. Xu, S. Yu, Least square completion and inconsistency repair methods for additively consistent fuzzy preference relations, Fuzzy Sets and Systems 198 (2012) 1–19.
  • (18) Y. Xu, K. W. Li, H. Wang, Distance-based consensus models for fuzzy and multiplicative preference relations, Information Sciences 253 (2013) 56–73.
  • (19) M. Xia, Z. Xu, H. Liao, Preference relations based on intuitionistic multiplicative information, IEEE Transactions on Fuzzy Systems 21 (1) (2013) 113–133.
  • (20) Y.-J. Wang, A fuzzy multi-criteria decision-making model by associating technique for order preference by similarity to ideal solution with relative preference relation, Information Sciences 268 (0) (2014) 169–184.
  • (21) E. Herrera-Viedma, S. Alonso, F. Chiclana, F. Herrera, A consensus model for group decision making with incomplete fuzzy preference relations, IEEE Transactions on Fuzzy Systems 15 (5) (2007) 863–877.
  • (22) F. Liu, W.-G. Zhang, J.-H. Fu, A new method of obtaining the priority weights from an interval fuzzy preference relation, Information Sciences 185 (1) (2012) 32–42.
  • (23) F. Chiclana, F. Herrera, E. Herrera-Viedma, A note on the internal consistency of various preference representations, Fuzzy Sets and Systems 131 (1) (2002) 75–78.
  • (24) Y. Xu, R. Patnayakuni, H. Wang, The ordinal consistency of a fuzzy preference relation, Information Sciences 224 (2013) 152–164.
  • (25) M. Xia, Z. Xu, J. Chen, Algorithms for improving consistency or consensus of reciprocal [0,1]-valued preference relations, Fuzzy Sets and Systems 216 (2013) 108–133.
  • (26) E. Herrera-Viedma, F. Herrera, F. Chiclana, M. Luque, Some issues on consistency of fuzzy preference relations, European Journal of Operational Research 154 (1) (2004) 98–109.
  • (27) T. Tanino, Fuzzy preference relations in group decision making, in: Non-conventional preference relations in decision making, Springer, 1988, pp. 54–71.
  • (28) T.-C. Wang, Y.-H. Chen, Applying consistent fuzzy preference relations to partnership selection, Omega 35 (4) (2007) 384–388.
  • (29) Y.-H. Chen, R.-J. Chao, Supplier selection using consistent fuzzy preference relations, Expert Systems with Applications 39 (3) (2012) 3233–3240.
  • (30) J. Lan, M. Hu, X. Ye, S. Sun, Deriving interval weights from an interval multiplicative consistent fuzzy preference relation, Knowledge-Based Systems 26 (2012) 128–134.
  • (31) Y. Deng, D numbers: Theory and applications, Journal of Information and Computational Science 9 (9) (2012) 2421–2428.
  • (32) X. Deng, Y. Hu, Y. Deng, S. Mahadevan, Supplier selection using AHP methodology extended by D numbers, Expert Systems with Applications 41 (1) (2014) 156–167.
  • (33) X. Deng, Y. Hu, Y. Deng, S. Mahadevan, Environmental impact assessment based on D numbers, Expert Systems with Applications 41 (2) (2014) 635–643.
  • (34) X. Deng, Y. Hu, Y. Deng, Bridge condition assessment using D numbers, The Scientific World Journal 2014 (2014) Article ID 358057, 11 pages. doi:10.1155/2014/358057.
  • (35) A. Dempster, Upper and lower probabilities induced by a multivalued mapping, Annals of Mathematics and Statistics 38 (2) (1967) 325–339.
  • (36) G. Shafer, A Mathematical Theory of Evidence, Princeton University Press, Princeton, 1976.
  • (37)

    W. Liu, Analyzing the degree of conflict among belief functions, Artificial Intelligence 170 (11) (2006) 909–924.

  • (38) J. Schubert, Conflict management in Dempster-Shafer theory using the degree of falsity, International Journal of Approximate Reasoning 52 (3) (2011) 449–460.
  • (39) A.-L. Jousselme, P. Maupin, Distances in evidence theory: Comprehensive survey and generalizations, International Journal of Approximate Reasoning 53 (2) (2012) 118–145.
  • (40) J.-B. Yang, D.-L. Xu, Evidential reasoning rule for evidence combination, Artificial Intelligence 205 (2013) 1–29.
  • (41) X. Deng, Y. Deng, On the axiomatic requirement of range to measure uncertainty, Physica A: Statistical Mechanics and its Applications - (-) (2014) –. doi:10.1016/j.physa.2014.03.060.
  • (42) D. Dubois, T. Denoeux, Conditioning in Dempster-Shafer theory: Prediction vs. Revision, in: Belief Functions: Theory and Applications, 2012, pp. 385–392.
  • (43) E. Lefevre, Z. Elouedi, How to preserve the conflict as an alarm in the combination of belief functions?, Decision Support Systems 56 (2013) 326–333.
  • (44) J. Dezert, A. Tchamova, On the validity of Dempster’s fusion rule and its interpretation as a generalization of Bayesian fusion rule, International Journal of Intelligent Systems 29 (3) (2014) 223–252.
  • (45) Y. Zhang, X. Deng, D. Wei, Y. Deng, Assessment of E-Commerce security using AHP and evidential reasoning, Expert Systems with Applications 39 (3) (2012) 3611–3623.
  • (46) A. Bolar, S. Tesfamariam, R. Sadiq, Condition assessment for bridges: a hierarchical evidential reasoning (HER) framework, Structure and Infrastructure Engineering 9 (7) (2013) 648–666.
  • (47) J.-B. Yang, J. Liu, J. Wang, H.-S. Sii, H.-W. Wang, Belief rule-base inference methodology using the evidential reasoning approach - RIMER, IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 36 (2) (2006) 266–285.
  • (48) Z.-G. Zhou, F. Liu, L.-C. Jiao, Z.-J. Zhou, J.-B. Yang, M.-G. Gong, X.-P. Zhang, A bi-level belief rule based decision support system for diagnosis of lymph node metastasis in gastric cancer, Knowledge-Based Systems 54 (2013) 128–136.
  • (49) T. Denoeux, M.-H. Masson, EVCLUS: evidential clustering of proximity data, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 34 (1) (2004) 95–109.
  • (50)

    Y. Bi, J. Guan, D. Bell, The combination of multiple classifiers using an evidential reasoning approach, Artificial Intelligence 172 (15) (2008) 1731–1751.

  • (51) T. Denoeux, Maximum likelihood estimation from uncertain data in the belief function framework, IEEE Transactions on Knowledge and Data Engineering 25 (1) (2013) 119–130.
  • (52) M. Casanovas, J. M. Merigo, Fuzzy aggregation operators in decision making with Dempster-Shafer belief structure, Expert Systems with Applications 39 (8) (2012) 7138–7149.
  • (53) K. Mokhtari, J. Ren, C. Roberts, J. Wang, Decision support framework for risk management on sea ports and terminals using fuzzy set theory and evidential reasoning approach, Expert Systems with Applications 39 (5) (2012) 5087–5103.
  • (54) R. R. Yager, N. Alajlan, Decision making with ordinal payoffs under Dempster-Shafer type uncertainty, International Journal of Intelligent Systems 28 (11) (2013) 1039–1053.
  • (55)

    R. P. Srivastava, L. Gao, P. R. Gillett, Representation of interrelationships among binary variables under Dempster-Shafer theory of belief functions, International Journal of Intelligent Systems 24 (4) (2009) 459–475.

  • (56) J. Klein, O. Colot, A belief function model for pixel data, in: Belief Functions: Theory and Applications, 2012, pp. 189–196.
  • (57) F. Cuzzolin, The geometry of consonant belief functions: simplicial complexes of necessity measures, Fuzzy Sets and Systems 161 (10) (2010) 1459–1479.
  • (58) F. Cuzzolin, On the relative belief transform, International Journal of Approximate Reasoning 53 (5) (2012) 786–804.
  • (59) B. Kang, Y. Deng, R. Sadiq, S. Mahadevan, Evidential cognitive maps, Knowledge-Based Systems 35 (2012) 77–86.
  • (60) D. Wei, X. Deng, X. Zhang, Y. Deng, S. Mahadevan, Identifying influential nodes in weighted networks based on evidence theory, Physica A: Statistical Mechanics and its Applications 392 (10) (2013) 2564–2575.
  • (61) X. Deng, Q. Liu, Y. Hu, Y. Deng, TOPPER: Topology prediction of transmembrane protein based on evidential reasoning, The Scientific World Journal 2013 (2013) Article ID 123731, 8 pages. doi:10.1155/2013/123731.