Topic
Pairwise comparison
About: Pairwise comparison is a research topic. Over the lifetime, 6804 publications have been published within this topic receiving 174081 citations.
Papers published on a yearly basis
Papers
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TL;DR: This paper addresses the Few-Shot Fine-Grained (FSFG) classification problem, which focuses on tackling the fine-grained classification under the challenging few-shot learning setting, and proposes a novel low-rank pairwise bilinear pooling operation to capture the nuanced differences between the support and query images for learning an effective distance metric.
Abstract: Deep neural networks have demonstrated advanced abilities on various visual classification tasks, which heavily rely on the large-scale training samples with annotated ground-truth. However, it is unrealistic always to require such annotation in real-world applications. Recently, Few-Shot learning (FS), as an attempt to address the shortage of training samples, has made significant progress in generic classification tasks. Nonetheless, it is still challenging for current FS models to distinguish the subtle differences between fine-grained categories given limited training data. To filling the classification gap, in this paper, we address the Few-Shot Fine-Grained (FSFG) classification problem, which focuses on tackling the fine-grained classification under the challenging few-shot learning setting. A novel low-rank pairwise bilinear pooling operation is proposed to capture the nuanced differences between the support and query images for learning an effective distance metric. Moreover, a feature alignment layer is designed to match the support image features with query ones before the comparison. We name the proposed model Low-Rank Pairwise Alignment Bilinear Network (LRPABN), which is trained in an end-to-end fashion. Comprehensive experimental results on four widely used fine-grained classification data sets demonstrate that our LRPABN model achieves the superior performances compared to state-of-the-art methods.
54 citations
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TL;DR: Methods for categorizing compounds into groups or series based on their ring-system content, using precalculated structure-based hashcodes and an indepen- dent technique for diversity assessment called the ’saturation diversity‘ approach are developed.
Abstract: We present some new ideas for characterizing and comparing largechemical databases. The comparison of the contents of large databases is nottrivial since it implies pairwise comparison of hundreds of thousands ofcompounds. We have developed methods for categorizing compounds into groupsor series based on their ring-system content, using precalculatedstructure-based hashcodes. Two large databases can then be compared bysimply comparing their hashcode tables. Furthermore, the number of distinctring-system combinations can be used as an indicator of database diversity.We also present an indepen- dent technique for diversity assessment calledthe ’saturation diversity‘ approach. This method is based on picking as manymutually dissimilar compounds as possible from a database or a subsetthereof. We show that both methods yield similar results. Since the twomethods measure very different properties, this probably says more about theproperties of the databases studied than about the methods.
54 citations
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29 Apr 2013TL;DR: This paper introduces techniques to resolve the top ranked items using signicantly fewer than all the possible pairwise comparisons using both random and adaptive sampling methodologies.
Abstract: Given a collection of N items with some unknown underlying ranking, we examine how to use pairwise comparisons to determine the top ranked items in the set. Resolving the top items from pairwise comparisons has application in diverse elds ranging from recommender systems to image-based search to protein structure analysis. In this paper we introduce techniques to resolve the top ranked items using signicantly fewer than all the possible pairwise comparisons using both random and adaptive sampling methodologies. Using randomly-chosen comparisons, a graph-based technique is shown to eciently resolve the top O(logN) items when there are no comparison errors. In terms of adaptively-chosen comparisons, we show how the top O(logN) items can be found, even in the presence of corrupted observations, using a voting methodology that only requires O N log 2 N pairwise comparisons.
54 citations
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TL;DR: In this article, the authors developed a new approach, using information available in the intermediate and final phases of the analytic hierarchy process, to explicitly identify which attributes or criteria are determinant in making a choice among several given alternatives.
Abstract: This article develops a new approach, using information available in the intermediate and final phases of the analytic hierarchy process, to explicitly identify which attributes or criteria are determinant in making a choice among several given alternatives. The approach parallels that used in the popular direct dual questioning determinant attribute (DQDA) analysis, which has been widely used in marketing applications. Using the hierarchical structure and pairwise comparisons, the combined relative priorities of the criteria are compared with the relative priorities of the choice alternatives to compute determinance scores. These values are the basis for identifying which of the criteria are both important and different across alternatives (i.e., determinant). This new approach overcomes the potential ambiguities of traditional direct dual questioning methods. Moreover, the approach is easily extended to include decision hierarchies with multiple levels of attributes and subattributes.
54 citations
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TL;DR: The results of this study indicate that there is no effect of differences in weights on value estimates at the group level and weight elicitation through pairwise comparison of criteria is preferred when taking into account its superior ability to discriminate between criteria and respondents’ preferences.
Abstract: Background
There is an increased interest in the use of multi-criteria decision analysis (MCDA) to support regulatory and reimbursement decision making. The EVIDEM framework was developed to provide pragmatic multi-criteria decision support in health care, to estimate the value of healthcare interventions, and to aid in priority-setting. The objectives of this study were to test 1) the influence of different weighting techniques on the overall outcome of an MCDA exercise, 2) the discriminative power in weighting different criteria of such techniques, and 3) whether different techniques result in similar weights in weighting the criteria set proposed by the EVIDEM framework.
Methods
A sample of 60 Dutch and Canadian students participated in the study. Each student used an online survey to provide weights for 14 criteria with two different techniques: a five-point rating scale and one of the following techniques selected randomly: ranking, point allocation, pairwise comparison and best worst scaling.
Results
The results of this study indicate that there is no effect of differences in weights on value estimates at the group level. On an individual level, considerable differences in criteria weights and rank order occur as a result of the weight elicitation method used, and the ability of different techniques to discriminate in criteria importance. Of the five techniques tested, the pair-wise comparison of criteria has the highest ability to discriminate in weights when fourteen criteria are compared.
Conclusions
When weights are intended to support group decisions, the choice of elicitation technique has negligible impact on criteria weights and the overall value of an innovation. However, when weights are used to support individual decisions, the choice of elicitation technique influences outcome and studies that use dissimilar techniques cannot be easily compared. Weight elicitation through pairwise comparison of criteria is preferred when taking into account its superior ability to discriminate between criteria and respondents’ preferences
54 citations