A Distance-Based Collective Preorder Integrating the Relative Importance of the Group's Members

Jabeur, Khaled; Martel, Jean‐Marc; Khelifa, S.B.

doi:10.1023/b:grup.0000042894.00775.75

Cited by 74 publications

(48 citation statements)

References 25 publications

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“…Previous literature failed to resolve such problems: Jabeur et al (2004), Jabeur and Martel (2010) and Amor et al (2007) concluded only that A 1 A 3 , missing some useful information, it is not conducive to information mining, and thus, has limited significance for scientific decision making.…”

Section: S(d(a L mentioning

confidence: 99%

“…González-Pachón and Romero (2001) aggregated partial ordinal rankings using an interval goal programming method. Jabeur et al (2004Jabeur et al ( , 2012, Martel (2007a, b, 2010), studied aggregation procedure, ordinal sorting, weight-determining method, and the decision problem with collective preorder (or reference preorder) preference structure based on the distance measure suggested by Roy and Slowinski (1993) and Jabeur and Martel (2010) also proposed an index to measure the agreement level of an individual preorder with respect to a collective preorder (or reference preorder) and derived binary mathematical programming based on the minimum distance of a collective preorder. Jullien-Ramasso et al (2012) presented a decision support system for animated films for the International Animated Film Festival organized at Annecy, France, with selection based on the multi-criteria aggregation of referees' ordinal preferences.…”

Section: Introductionmentioning

confidence: 99%

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Lattice Order Group Decision Making with Interval Probability Based on Prospect Theory

Guo

Peng

2014

Group Decis Negot

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A random lattice order decision analysis method is proposed based on an interval probability distribution preference vector by way of entropy theory, focusing on a decision preference system in which preference relation probability is described by interval values and the decision maker's behavior is also considered. The preference characterization of decision makers is extended from four varieties of relations to seven varieties of preference relations. In addition to the concept, property, and operation rules of interval probability, the concept of interval-valued distribution preference vectors and the relative entropy on the lattice-ordered preference system are given. Then, the interval probability can be more precisely determined, and the weighting interval probability is transformed into the interval probability weight. The ER nonlinear optimization model based on preference entropy is established, individual preferences are aggregated by applying the priority rule and the intersection rule, and the specific steps of decision making are given. Finally, the feasibility and effectiveness of the approach proposed in this paper are illustrated with a numerical example.

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Section: S(d(a L mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Lattice Order Group Decision Making with Interval Probability Based on Prospect Theory

Guo

Peng

2014

Group Decis Negot

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“…Keeney and Raiffa (1976) employ cardinal utility functions to accumulate group preferences. Jabeur et al (2004) and See and Lewis (2006) further assume unequal weights for the preferences of the group members.…”

Section: Group Preference Aggregationmentioning

confidence: 99%

“…If there is no hierarchy and no apparent leader in the team, the weightings can simply be assumed equal or proportional to an individual's utterances in the discussion. Weightings may be adjusted to reflect information such as an individual's leadership, expertise, and member importance (Jabeur et al 2004;See and Lewis 2006).…”

Section: Approach 1: Translating Survey Data Into Preferential Probabmentioning

confidence: 99%

An approach to the extraction of preference-related information from design team language

Yang

Honda

2011

Res Eng Design

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The process of selecting among design alternatives is an important activity in the early stages of design. A designer is said to express design preferences when assigning priorities to a set of possible design choices. However, the assignment of preferences becomes more challenging on both a practical and theoretical level when performed by a group. This paper presents a probabilistic approach for estimating a team's overall preference-related information known as preferential probabilities that extracts information from the natural language used in team discussion transcripts without aggregation of individual team member opinions. Assessment of the method is conducted by surveying a design team to obtain quantitative ratings of alternatives. Two different approaches are applied to convert these ratings into values that may be compared to the results of transcript analysis: the application of a modified Logit model and simulation based on the principle of maximum entropy. The probabilistic approach proposed in the paper represents how likely a choice is to be ''most preferred'' by a design team over a given period of time. A preliminary design selection experiment was conducted as an illustrative case example of the method.Correlations were found between the preferential probabilities estimated from transcripts and those computed from the surveyed preferences. The proposed methods may provide a formal way to understand and represent informal, unstructured design information using a low overhead information extraction method.

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“…This algorithm is contributing to the resolution of the problem of clustering in a multidimensional way by using algorithm DBSCAN [25] and integrating a model of similarity inspired of the concept of the multiple-criteria decision analysis [26], [27] [28] [29] [30] [31]. This approach based on the density makes it possible to work on great databases without however determining beforehand the nature and the number of clusters, in this family of clustering much of work exists, quoting by way an example algorithm DGLC [32], OPTICS [33], DENCLUE [34], WaveCluster [35], CLICKS [36], CURD [37] AND DBSCAN [38].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Criteria Decision Method in the DBSCAN Algorithm for Better Clustering

Idrissi¹,

Alaoui²

2016

ijacsa

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Abstract-This paper presents a solution based on the unsupervised classification for the multiple-criteria analysis problems of data, where the characteristics and the number of clusters are not predefined, and the objects of data sets are described by several criteria, and the latter can be contradictory, of different nature and varied weights. This work focuses on two different tracks of research, the unsupervised classification which is one of data mining techniques as well as the multi-criteria clustering which is part of the field of Multiple-criteria decisionmaking. Experimental results on different data sets are presented in order to show that clusters, formed using the improvement of the algorithm DBSCAN by incorporating a model of similarity, are intensive and accurate.

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A Distance-Based Collective Preorder Integrating the Relative Importance of the Group's Members

Cited by 74 publications

References 25 publications

Lattice Order Group Decision Making with Interval Probability Based on Prospect Theory

Lattice Order Group Decision Making with Interval Probability Based on Prospect Theory

An approach to the extraction of preference-related information from design team language

A Multi-Criteria Decision Method in the DBSCAN Algorithm for Better Clustering

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