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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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Journal ArticleDOI
TL;DR: In this paper, it was shown that pairwise comparison charts (PCC) provide results that are identical to those obtained by the Borda count, and that the PCC is thus not subject to the arguments used against non-Borda count methods.
Abstract: Designers routinely rank alternatives in a variety of settings using a staple of comparison, the pairwise comparison. In recent years questions have been raised about the use of such comparisons as a means of calculating and aggregating meaningful preference or choice data. Results on voting have been used to argue that the positional procedure known as the Borda count is the best pairwise voting procedure, or at least the only one that is not subject to a number of demonstrable problems. We show here that pairwise comparison charts (PCC) provide results that are identical to those obtained by the Borda count, and that the PCC is thus not subject to the arguments used against non-Borda count methods. Arrow's impossibility theorem has also been invoked to cast doubt upon any pairwise procedure, including the Borda count. We discuss the relevance of the Arrow property that is lost in the Borda count, the independence of irrelevant alternatives (IIA). While the theoretical consequences of IIA are devastating, it is not clear that the same is true of its practical consequences. Examples are presented to illustrate some primary objections to pairwise methods.

120 citations

Journal ArticleDOI
TL;DR: Fuzzy analytic network process (FANP) based methodology is discussed to tackle the different decision criteria involved in the selection of competitive priorities in current business scenario and can provide a hierarchical framework for the cleaner production implemented organization to select on its competitive priorities.
Abstract: The aim of this paper is to identify and discuss some of the important and critical decision criteria including cleaner production implementation of an efficient system to prioritize competitive priorities. Fuzzy analytic network process (FANP) based methodology is discussed to tackle the different decision criteria involved in the selection of competitive priorities in current business scenario. FANP is an efficient tool to handle the fuzziness of the data involved in deciding the preferences of different decision variables. The linguistic level of comparisons produced by the professionals and experts for each comparison are tapped in the form of triangular fuzzy numbers to construct fuzzy pairwise comparison matrices. The implementation of the system is demonstrated by a problem having four stages of hierarchy which contains different criteria, attributes and alternatives at wider perspective. The proposed model can provide a hierarchical framework for the cleaner production implemented organization to select on its competitive priorities.

119 citations

Journal ArticleDOI
TL;DR: Lexicographic Goal Programming (LGP) has been used to find out weights from pairwise inconsistent interval judgment matrices and a number of properties and advantages of LGP as a weight determination technique have been explored.

119 citations

Journal ArticleDOI
TL;DR: A new extension of the ELECTRE, known as the elimination and choice translating reality method, for multi-criteria group decision-making problems based on intuitionistic fuzzy sets is designed and a new discordance intuitionistic index is introduced, which is extended from the concept of the fuzzy distance measure.

119 citations

Journal Article
TL;DR: This article proposes a generic framework for computation of similarity measures for sequences, covering various kernel, distance and non-metric similarity functions, and provides linear-time algorithms of different complexity and capabilities using sorted arrays, tries and suffix trees as underlying data structures.
Abstract: Efficient and expressive comparison of sequences is an essential procedure for learning with sequential data. In this article we propose a generic framework for computation of similarity measures for sequences, covering various kernel, distance and non-metric similarity functions. The basis for comparison is embedding of sequences using a formal language, such as a set of natural words, k-grams or all contiguous subsequences. As realizations of the framework we provide linear-time algorithms of different complexity and capabilities using sorted arrays, tries and suffix trees as underlying data structures. Experiments on data sets from bioinformatics, text processing and computer security illustrate the efficiency of the proposed algorithms---enabling peak performances of up to 106 pairwise comparisons per second. The utility of distances and non-metric similarity measures for sequences as alternatives to string kernels is demonstrated in applications of text categorization, network intrusion detection and transcription site recognition in DNA.

119 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20241
20231,305
20222,607
2021581
2020554
2019520