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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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Book ChapterDOI
05 Sep 2005
TL;DR: An exact model based on Markov chains is proposed to formulate the variation of gene frequency and reveals the pairwise equivalence phenomenon in the number of parents and indicates the acceleration of genetic drift in MPGAs.
Abstract: This paper investigates genetic drift in multi-parent genetic algorithms (MPGAs). An exact model based on Markov chains is proposed to formulate the variation of gene frequency. This model identifies the correlation between the adopted number of parents and the mean convergence time. Moreover, it reveals the pairwise equivalence phenomenon in the number of parents and indicates the acceleration of genetic drift in MPGAs. The good fit between theoretical and experimental results further verifies the capability of this model.

61 citations

Journal ArticleDOI
01 Apr 2011
TL;DR: This work aims at assessing the suitability of multi-criteria decision making (MCDM) methods to support software engineers' decisions by proposing a HAM (Hybrid Assessment Method), which gives its user the ability to perceive the influence different decisions may have on the final result.
Abstract: During software development, many decisions need to be made to guarantee the satisfaction of the stakeholders' requirements and goals. The full satisfaction of all of these requirements and goals may not be possible, requiring decisions over conflicting human interests as well as technological alternatives, with an impact on the quality and cost of the final solution. This work aims at assessing the suitability of multi-criteria decision making (MCDM) methods to support software engineers' decisions. To fulfil this aim, a HAM (Hybrid Assessment Method) is proposed, which gives its user the ability to perceive the influence different decisions may have on the final result. HAM is a simple and efficient method that combines one single pairwise comparison decision matrix (to determine the weights of criteria) with one classical weighted decision matrix (to prioritize the alternatives). To avoid consistency problems regarding the scale and the prioritization method, HAM uses a geometric scale for assessing the criteria and the geometric mean for determining the alternative ratings.

60 citations

Proceedings ArticleDOI
01 Jan 2013
TL;DR: A new and relaxed assumption of pairwise preferences over item-sets is proposed, which defines a user’s preference on a set of items (item-set) instead of on a single item, and a general algorithm called CoFiSet is developed, which performs better than several state of theart methods on various ranking-oriented evaluation metrics on two real-world data sets.
Abstract: Collaborative filtering aims to make use of users’ feedbacks to improve the recommendation performance, which has been deployed in various industry recommender systems. Some recent works have switched from exploiting explicit feedbacks of numerical ratings to implicit feedbacks like browsing and shopping records, since such data are more abundant and easier to collect. One fundamental challenge of leveraging implicit feedbacks is the lack of negative feedbacks, because there are only some observed relatively “positive” feedbacks, making it difficult to learn a prediction model. Previous works address this challenge via proposing some pointwise or pairwise preference assumptions on items. However, such assumptions with respect to items may not always hold, for example, a user may dislike a bought item or like an item not bought yet. In this paper, we propose a new and relaxed assumption of pairwise preferences over item-sets, which defines a user’s preference on a set of items (item-set) instead of on a single item. The relaxed assumption can give us more accurate pairwise preference relationships. With this assumption, we further develop a general algorithm called CoFiSet (collaborative filtering via learning pairwise preferences over item-sets). Experimental results show that CoFiSet performs better than several stateof-the-art methods on various ranking-oriented evaluation metrics on two real-world data sets. Furthermore, CoFiSet is very efficient as shown by both the time complexity and CPU time.

60 citations

Book ChapterDOI
25 Apr 2010
TL;DR: It is shown that pairwise Cardinality networks are superior to the cardinality networks introduced in previous work which are derived from odd-even sorting networks, which express cardinality constraints.
Abstract: We introduce pairwise cardinality networks, networks of comparators, derived from pairwise sorting networks, which express cardinality constraints. We show that pairwise cardinality networks are superior to the cardinality networks introduced in previous work which are derived from odd-even sorting networks. Our presentation identifies the precise relationship between odd-even and pairwise sorting networks. This relationship also clarifies why pairwise sorting networks have significantly better propagation properties for the application of cardinality constraints.

60 citations

Journal ArticleDOI
18 May 2011-PLOS ONE
TL;DR: Simulation showed that the jackknife performs better than the bootstrap at accurately estimating confidence intervals for pairwise agreement measures, especially when the agreement between partitions is low.
Abstract: Several research fields frequently deal with the analysis of diverse classification results of the same entities. This should imply an objective detection of overlaps and divergences between the formed clusters. The congruence between classifications can be quantified by clustering agreement measures, including pairwise agreement measures. Several measures have been proposed and the importance of obtaining confidence intervals for the point estimate in the comparison of these measures has been highlighted. A broad range of methods can be used for the estimation of confidence intervals. However, evidence is lacking about what are the appropriate methods for the calculation of confidence intervals for most clustering agreement measures. Here we evaluate the resampling techniques of bootstrap and jackknife for the calculation of the confidence intervals for clustering agreement measures. Contrary to what has been shown for some statistics, simulations showed that the jackknife performs better than the bootstrap at accurately estimating confidence intervals for pairwise agreement measures, especially when the agreement between partitions is low. The coverage of the jackknife confidence interval is robust to changes in cluster number and cluster size distribution.

60 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