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Ranking Problem (ranking + problem)

Distribution by Scientific Domains
Business, Economics, Finance and Accounting60%

Selected Abstracts

The Selection of Multiattribute Decision Making Methods for Scholarship Student Selection

INTERNATIONAL JOURNAL OF SELECTION AND ASSESSMENT, Issue 4 2003
Chung-Hsing Yeh
Selecting scholarship students from a number of competing candidates is a complex decision making process, in which multiple selection criteria have to be considered simultaneously. Multiattribute decision making (MADM) has proven to be an effective approach for ranking or selecting one or more alternatives from a finite number of alternatives with respect to multiple, usually conflicting criteria. This paper formulates the scholarship student selection process as an MADM problem, and presents suitable compensatory methods for solving the problem. A new empirical validity procedure is developed to deal with the inconsistent ranking problem caused by different MADM methods. The procedure aims at selecting a ranking outcome which has a minimum expected value loss, when true attribute weights are not known. An empirical study of a scholarship student selection problem in an Australian university is conducted to illustrate how the selection procedure works. [source]

A new rank correlation coefficient with application to the consensus ranking problem

JOURNAL OF MULTI CRITERIA DECISION ANALYSIS, Issue 1 2002
Edward J. Emond
Abstract The consensus ranking problem has received much attention in the statistical literature. Given m rankings of n objects the objective is to determine a consensus ranking. The input rankings may contain ties, be incomplete, and may be weighted. Two solution concepts are discussed, the first maximizing the average weighted rank correlation of the solution ranking with the input rankings and the second minimizing the average weighted Kemeny,Snell distance. A new rank correlation coefficient called ,x is presented which is shown to be the unique rank correlation coefficient which is equivalent to the Kemeny-Snell distance metric. The new rank correlation coefficient is closely related to Kendall's tau but differs from it in the way ties are handled. It will be demonstrated that Kendall's ,b is flawed as a measure of agreement between weak orderings and should no longer be used as a rank correlation coefficient. The use of ,x in the consensus ranking problem provides a more mathematically tractable solution than the Kemeny,Snell distance metric because all the ranking information can be summarized in a single matrix. The methods described in this paper allow analysts to accommodate the fully general consensus ranking problem with weights, ties, and partial inputs. Copyright © 2002 John Wiley & Sons, Ltd. [source]

II,On Modelling Vagueness,and on not Modelling Incommensurability

ARISTOTELIAN SOCIETY SUPPLEMENTARY VOLUME, Issue 1 2009
Robert Sugden
This paper defines and analyses the concept of a ,ranking problem'. In a ranking problem, a set of objects, each of which possesses some common property P to some degree, are ranked by P-ness. I argue that every eligible answer to a ranking problem can be expressed as a complete and transitive ,is at least as P as' relation. Vagueness is expressed as a multiplicity of eligible rankings. Incommensurability, properly understood, is the absence of a common property P. Trying to analyse incommensurability in the same framework as ranking problems causes unnecessary confusion. [source]

Growing decision trees in an ordinal setting

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 7 2003
Kim Cao-Van
Although ranking (ordinal classification/regression) based on criteria is related closely to classification based on attributes, the development of methods for learning a ranking on the basis of data is lagging far behind that for learning a classification. Most of the work being done focuses on maintaining monotonicity (sometimes even only on the training set). We argue that in doing so, an essential aspect is mostly disregarded, namely, the importance of the role of the decision maker who decides about the acceptability of the generated rule base. Certainly, in ranking problems, there are more factors besides accuracy that play an important role. In this article, we turn to the field of multicriteria decision aid (MCDA) in order to cope with the aforementioned problems. We show that by a proper definition of the notion of partial dominance, it is possible to avoid the counter-intuitive outcomes of classification algorithms when applied to ranking problems. We focus on tree-based approaches and explain how the tree expansion can be guided by the principle of partial dominance preservation, and how the resulting rule base can be graphically represented and further refined. © 2003 Wiley Periodicals, Inc. [source]