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Pairwise comparison

About: Pairwise comparison is a(n) research topic. Over the lifetime, 6804 publication(s) have been published within this topic receiving 174081 citation(s).

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Open accessJournal Article
Thomas L. Saaty1Institutions (1)
01 Jan 2002-Scientia Iranica
Abstract: Decisions involve many intangibles that need to be traded off. To do that, they have to be measured along side tangibles whose measurements must also be evaluated as to, how well, they serve the objectives of the decision maker. The Analytic Hierarchy Process (AHP) is a theory of measurement through pairwise comparisons and relies on the judgements of experts to derive priority scales. It is these scales that measure intangibles in relative terms. The comparisons are made using a scale of absolute judgements that represents, how much more, one element dominates another with respect to a given attribute. The judgements may be inconsistent, and how to measure inconsistency and improve the judgements, when possible to obtain better consistency is a concern of the AHP. The derived priority scales are synthesised by multiplying them by the priority of their parent nodes and adding for all such nodes. An illustration is included.

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5,663 Citations


Journal ArticleDOI: 10.1504/IJSSCI.2008.017590
Thomas L. Saaty1Institutions (1)
Abstract: Decisions involve many intangibles that need to be traded off To do that, they have to be measured along side tangibles whose measurements must also be evaluated as to, how well, they serve the objectives of the decision maker The Analytic Hierarchy Process (AHP) is a theory of measurement through pairwise comparisons and relies on the judgements of experts to derive priority scales It is these scales that measure intangibles in relative terms The comparisons are made using a scale of absolute judgements that represents, how much more, one element dominates another with respect to a given attribute The judgements may be inconsistent, and how to measure inconsistency and improve the judgements, when possible to obtain better consistency is a concern of the AHP The derived priority scales are synthesised by multiplying them by the priority of their parent nodes and adding for all such nodes An illustration is included

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5,626 Citations


Open accessJournal ArticleDOI: 10.5555/1390681.1442798
Abstract: Consider data consisting of pairwise measurements, such as presence or absence of links between pairs of objects. These data arise, for instance, in the analysis of protein interactions and gene regulatory networks, collections of author-recipient email, and social networks. Analyzing pairwise measurements with probabilistic models requires special assumptions, since the usual independence or exchangeability assumptions no longer hold. Here we introduce a class of variance allocation models for pairwise measurements: mixed membership stochastic blockmodels. These models combine global parameters that instantiate dense patches of connectivity (blockmodel) with local parameters that instantiate node-specific variability in the connections (mixed membership). We develop a general variational inference algorithm for fast approximate posterior inference. We demonstrate the advantages of mixed membership stochastic blockmodels with applications to social networks and protein interaction networks.

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1,780 Citations


Proceedings ArticleDOI: 10.1145/1273496.1273513
Zhe Cao1, Tao Qin1, Tie-Yan Liu2, Ming-Feng Tsai3  +1 moreInstitutions (3)
20 Jun 2007-
Abstract: The paper is concerned with learning to rank, which is to construct a model or a function for ranking objects. Learning to rank is useful for document retrieval, collaborative filtering, and many other applications. Several methods for learning to rank have been proposed, which take object pairs as 'instances' in learning. We refer to them as the pairwise approach in this paper. Although the pairwise approach offers advantages, it ignores the fact that ranking is a prediction task on list of objects. The paper postulates that learning to rank should adopt the listwise approach in which lists of objects are used as 'instances' in learning. The paper proposes a new probabilistic method for the approach. Specifically it introduces two probability models, respectively referred to as permutation probability and top k probability, to define a listwise loss function for learning. Neural Network and Gradient Descent are then employed as model and algorithm in the learning method. Experimental results on information retrieval show that the proposed listwise approach performs better than the pairwise approach.

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Topics: Learning to rank (65%), Semi-supervised learning (61%), Online machine learning (60%) ...read more

1,752 Citations


Open accessJournal ArticleDOI: 10.1023/A:1010920819831
David J. Hand1, Robert Till1Institutions (1)
18 Oct 2001-Machine Learning
Abstract: The area under the ROC curve, or the equivalent Gini index, is a widely used measure of performance of supervised classification rules. It has the attractive property that it side-steps the need to specify the costs of the different kinds of misclassification. However, the simple form is only applicable to the case of two classes. We extend the definition to the case of more than two classes by averaging pairwise comparisons. This measure reduces to the standard form in the two class case. We compare its properties with the standard measure of proportion correct and an alternative definition of proportion correct based on pairwise comparison of classes for a simple artificial case and illustrate its application on eight data sets. On the data sets we examined, the measures produced similar, but not identical results, reflecting the different aspects of performance that they were measuring. Like the area under the ROC curve, the measure we propose is useful in those many situations where it is impossible to give costs for the different kinds of misclassification.

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1,689 Citations


Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20229
2021494
2020516
2019514
2018462
2017449

Top Attributes

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Topic's top 5 most impactful authors

Waldemar W. Koczkodaj

45 papers, 973 citations

Salvatore Greco

27 papers, 1.9K citations

Eyke Hüllermeier

22 papers, 1K citations

Johannes Fürnkranz

22 papers, 1.2K citations

Ludmil Mikhailov

19 papers, 1.1K citations

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