Selection of a Representative Value Function for Robust Ordinal Regression in Group Decision Making

Kadziński, Miłosz; Greco, Salvatore; Słowiński, Roman

doi:10.1007/s10726-011-9277-z

Cited by 43 publications

(12 citation statements)

References 21 publications

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“…Greco et al [52] extended UTADIS GMS introducing the concept of the representative value function in robust ordinal regression with the aim of considering complete sets of instances of a preference model compatible with the information provided by the DM; it means that the representativeness of a selected value function is understood in the sense of robustness preoccupation. This method was also extended to group decision in [53] with the same considerations of the method proposed by [50], but considering the representative value function in robust ordinal regression.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

A Model for Sorting Activities to Be Outsourced in Civil Construction Based on ROR-UTADIS

Palha

Almeida

Alencar

2016

Mathematical Problems in Engineering

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The subcontractor’s selection problem is currently treated as a supply chain problem with a prequalification procedure to balance the main objectives of the client: cost, quality, and time. Unfortunately, most of the selection processes are analysed under the same methodology without considering that variations in project, type of activity, and other attributes should affect the chosen method. To provide a novel form of treating subcontractor’s selection, we proposed an additive sorting method to categorize activities to be outsourced in civil construction based on ROR-UTADIS method, which is a modification of the UTADIS method that includes new forms of supplying preference information. It was applied in the construction of a brewery in Brazil. It was perceived that the method is applicable and intuitive for decision makers, even though there are quite a few points to be taken, analysed to avoid misclassification.

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Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

A Model for Sorting Activities to Be Outsourced in Civil Construction Based on ROR-UTADIS

Palha

Almeida

Alencar

2016

Mathematical Problems in Engineering

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“…Consequently, if preferences of the DM do not violate dominance and are not contradictory, it is very likely that they could be reproduced by the model used in ROR. Nevertheless, ROR has been also adapted to group decision Kadziński et al 2013b). …”

Section: Performance Measurementioning

confidence: 99%

Robust ordinal regression in preference learning and ranking

et al. 2013

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Multiple Criteria Decision Aiding (MCDA) offers a diversity of approaches designed for providing the decision maker (DM) with a recommendation concerning a set of alternatives (items, actions) evaluated from multiple points of view, called criteria. This paper aims at drawing attention of the Machine Learning (ML) community upon recent advances in a representative MCDA methodology, called Robust Ordinal Regression (ROR). ROR learns by examples in order to rank a set of alternatives, thus considering a similar problem as Preference Learning (ML-PL) does. However, ROR implements the interactive preference construction paradigm, which should be perceived as a mutual learning of the model and the DM. The paper clarifies the specific interpretation of the concept of preference learning adopted in ROR and MCDA, comparing it to the usual concept of preference learning considered within ML. This comparison concerns a structure of the considered problem, types of admitted preference information, a character of the employed preference models, ways of exploiting them, and techniques to arrive at a final ranking.

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“…When it comes to an example-based value driven sorting procedure, classes are implicitly delimited by some assignment examples rather than threshold on a utility scale (see, e.g., [11,17]). Precisely, such a procedure is driven by a value function U and its associated assignment examples.…”

Section: Value-driven Sorting Proceduresmentioning

confidence: 99%

DIS-CARD: a new method of multiple criteria sorting to classes with desired cardinality

Kadziński

Słowiński

2012

J Glob Optim

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In this paper, we present a new preference disaggregation method for multiple criteria sorting problems, called DIS-CARD. Real-life experience indicates the need of considering decision making situations in which a decision maker (DM) specifies a desired number of alternatives to be assigned to single classes or to unions of some classes. These situations require special methods for multiple criteria sorting subject to desired cardinalities of classes. DIS-CARD deals with such a problem, using the ordinal regression approach to construct a model of DM's preferences from preference information provided in terms of exemplary assignments of some reference alternatives, together with the above desired cardinalities. We develop a mathematical model for incorporating such preference information via mixed integer linear programming (MILP). Then, we adapt the MILP model to two types of preference models: an additive value function and an outranking relation. Illustrative example is solved to illustrate the methodology.

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Selection of a Representative Value Function for Robust Ordinal Regression in Group Decision Making

Cited by 43 publications

References 21 publications

A Model for Sorting Activities to Be Outsourced in Civil Construction Based on ROR-UTADIS

A Model for Sorting Activities to Be Outsourced in Civil Construction Based on ROR-UTADIS

Robust ordinal regression in preference learning and ranking

DIS-CARD: a new method of multiple criteria sorting to classes with desired cardinality

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