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 paper, the authors use fuzzy information on the pairwise rank-ordering between the objectives, which often appears more reasonable than so-called 'crisp' quantitive statements, especially for practical problems.
53 citations
TL;DR: The extent to which the widespread use of the F1 renders empirical results in software defect prediction unreliable is understood, specifically the biased and misleading F1 metric should be deprecated.
Abstract: Context: There is considerable diversity in the range and design of computational experiments to assess classifiers for software defect prediction. This is particularly so, regarding the choice of classifier performance metrics. Unfortunately some widely used metrics are known to be biased, in particular F1. Objective: We want to understand the extent to which the widespread use of the F1 renders empirical results in software defect prediction unreliable. Method: We searched for defect prediction studies that report both F1 and the Matthews correlation coefficient (MCC). This enabled us to determine the proportion of results that are consistent between both metrics and the proportion that change. Results: Our systematic review identifies 8 studies comprising 4017 pairwise results. Of these results, the direction of the comparison changes in 23% of the cases when the unbiased MCC metric is employed. Conclusion: We find compelling reasons why the choice of classification performance metric matters, specifically the biased and misleading F1 metric should be deprecated.
53 citations
TL;DR: This work proposes a procedure to reduce the dimensions of multiplex network data measured in multiple groups by clustering the networks using their pairwise similarities, and constructing composite network measures as combinations of the networks in each resulting cluster.
53 citations
TL;DR: In the Analytic Hierarchy Process (AHP), decision makers make pairwise comparisons of alternatives and criteria as mentioned in this paper, and the results of the laboratory study with 180 participants confirm that the 1-to-9 conversion table, as is often used in the AHP, tends to overestimate differences in preference.
53 citations
TL;DR: The multiple attribute decision method known as analytical hierarchy process (AHP), which uses pairwise comparisons with numerical judgments, is considered, which has the major advantage of depending on just n decision variables – the number of compared elements – and so is less computationally expensive than other optimisation methods.
53 citations