Topic
Pairwise comparison
About: Pairwise comparison is a research topic. Over the lifetime, 6804 publications have been published within this topic receiving 174081 citations.
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TL;DR: In this article, an IVIF (interval-valued intuitionistic fuzzy) approach is proposed to deal with vagueness, ambiguity and subjectivity in the human evaluation processes.
139 citations
TL;DR: This study extends an efficient density-based algorithm for pairwise coverage to generate t-way interaction test suites and shows that it guarantees a logarithmic upper bound on the size of the test suites as a function of the number of factors.
Abstract: Algorithmic construction of software interaction test suites has focussed on pairwise coverage; less is known about the efficient construction of test suites for t-way interactions with t≥3. This study extends an efficient density-based algorithm for pairwise coverage to generate t-way interaction test suites and shows that it guarantees a logarithmic upper bound on the size of the test suites as a function of the number of factors. To complement this theoretical guarantee, an implementation is outlined and some practical improvements are made. Computational comparisons with other published methods are reported. Many of the results improve upon those in the literature. However, limitations on the ability of one-test-at-a-time algorithms are also identified. Copyright © 2008 John Wiley & Sons, Ltd.
139 citations
TL;DR: In this article, an item response theory (IRT) approach is proposed to construct and score multidimensional pairwise preference items using Monte Carlo simulations. But the results show that the MUPP approach to test construction and scoring provides accurate parameter recovery in both one-and two-dimensional simulations, even with relatively few (say, 15%) unidimensional pairs.
Abstract: This article proposes an item response theory (IRT) approach to constructing and scoring multidimensional pairwise preference items. Individual statements are administered and calibrated using a unidimensional single-stimulus model. Tests are created by combining multidimensional items with a small number of unidimensional pairings needed to identify the latent metric. Trait scores are then obtained using a multidimensional Bayes modal estimation procedure based on a mathematical model called MUPP, which is illustrated and tested here using Monte Carlo simulations. Simulation results show that the MUPP approach to test construction and scoring provides accurate parameter recovery in both one- and two-dimensional simulations, even with relatively few (say, 15%) unidimensional pairings. The implications of these results for constructing and scoring fake-resistant personality items are discussed.
139 citations
TL;DR: In this article, the authors proposed an adaptive AHP approach (A 3 ) that uses a soft computing scheme, Genetic Algorithms, to recover the real number weightings of the various criteria in AHP and provides a function for automatically improving the consistency ratio of pairwise comparisons.
139 citations
TL;DR: The proposed priorization procedure for Analytic Hierarchy Process Group Decision Making does not require intermediate filters for the actors’ initial judgements and provides more efficient estimates than the techniques conventionally applied in the literature for AHP-GDM.
139 citations