scispace - formally typeset
Search or ask a question
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
More filters
Journal ArticleDOI
TL;DR: This work considers the use of voting tree rules to aggregate agents’ preferences and proposes several heuristics that find in polynomial time a superset of the possible winners and a subset of the necessary winners which are based on the completions of the majority graph built from the incomplete profiles.
Abstract: In multiagent settings where agents have different preferences, preference aggregation can be an important issue. Voting is a general method to aggregate preferences. We consider the use of voting tree rules to aggregate agents' preferences. In a voting tree, decisions are taken by performing a sequence of pairwise comparisons in a binary tree where each comparison is a majority vote among the agents. Incompleteness in the agents' preferences is common in many real-life settings due to privacy issues or an ongoing elicitation process. We study how to determine the winners when preferences may be incomplete, not only for voting tree rules (where the tree is assumed to be fixed), but also for the Schwartz rule (in which the winners are the candidates winning for at least one voting tree). In addition, we study how to determine the winners when only balanced trees are allowed. In each setting, we address the complexity of computing necessary (respectively, possible) winners, which are those candidates winning for all completions (respectively, at least one completion) of the incomplete profile. We show that many such winner determination problems are computationally intractable when the votes are weighted. However, in some cases, the exact complexity remains unknown. Since it is generally computationally difficult to find the exact set of winners for voting trees and the Schwartz rule, we propose several heuristics that find in polynomial time a superset of the possible winners and a subset of the necessary winners which are based on the completions of the (incomplete) majority graph built from the incomplete profiles.

66 citations

Journal ArticleDOI
TL;DR: The proposed method of localizing the inconsistency may conceivably be of relevance for nonclassical logics (e.g., paraconsistent logic) and for uncertainty reasoning since it accommodates inconsistency by treating inconsistent data as still useful information.
Abstract: One of the major challenges for collective intelligence is inconsistency, which is unavoidable whenever subjective assessments are involved. Pairwise comparisons allow one to represent such subjective assessments and to process them by analyzing, quantifying and identifying the inconsistencies. We propose using smaller scales for pairwise comparisons and provide mathematical and practical justifications for this change. Our postulate's aim is to initiate a paradigm shift in the search for a better scale construction for pairwise comparisons. Beyond pairwise comparisons, the results presented may be relevant to other methods using subjective scales. Keywords: pairwise comparisons, collective intelligence, scale, subjective assessment, inaccuracy, inconsistency.

66 citations

Proceedings ArticleDOI
13 Aug 2016
TL;DR: A general probabilistic framework for predicting the outcome of pairwise matchups and pairwise preferences, both of which have widespread applications ranging from matchmaking in computer games to recommendation in e-commerce is presented.
Abstract: We present a general probabilistic framework for predicting the outcome of pairwise matchups (e.g. two-player sport matches) and pairwise preferences (e.g. product preferences), both of which have widespread applications ranging from matchmaking in computer games to recommendation in e-commerce. Unlike existing models for these tasks, our model not only learns representations of the items in a more expressive latent vector space, but also models how context modifies matchup and preference outcomes. For example, the context "weather" may alter the winning probability in a tennis match, or the fact that the user is on a mobile device may alter his preferences among restaurants. More generally, the model is capable of handling any symmetric game/comparison problem that can be described by vectorized player/item and game/context features. We provide a comprehensive evaluation of its predictive performance with real datasets from both domains to show its ability to predict preference and game outcomes more accurately than existing models. Furthermore, we demonstrate on synthetic datasets the expressiveness of the model when compared against theoretical limits.

66 citations

Journal ArticleDOI
TL;DR: A class of global potentials defined over all variables in the CRF can be readily optimised using standard graph cut algorithms at little extra expense compared to a standard pairwise field and can be directly used for the problem of class based image segmentation.
Abstract: The Markov and Conditional random fields (CRFs) used in computer vision typically model only local interactions between variables, as this is generally thought to be the only case that is computationally tractable. In this paper we consider a class of global potentials defined over all variables in the CRF. We show how they can be readily optimised using standard graph cut algorithms at little extra expense compared to a standard pairwise field. This result can be directly used for the problem of class based image segmentation which has seen increasing recent interest within computer vision. Here the aim is to assign a label to each pixel of a given image from a set of possible object classes. Typically these methods use random fields to model local interactions between pixels or super-pixels. One of the cues that helps recognition is global object co-occurrence statistics, a measure of which classes (such as chair or motorbike) are likely to occur in the same image together. There have been several approaches proposed to exploit this property, but all of them suffer from different limitations and typically carry a high computational cost, preventing their application on large images. We find that the new model we propose produces a significant improvement in the labelling compared to just using a pairwise model and that this improvement increases as the number of labels increases.

66 citations

Journal Article
TL;DR: Pairwise classification is the task to predict whether the examples a,b of a pair belong to the same class or to different classes, and particular kernels as well as the use of symmetric training sets in the framework of support vector machines were suggested.
Abstract: Pairwise classification is the task to predict whether the examples a,b of a pair (a,b) belong to the same class or to different classes. In particular, interclass generalization problems can be treated in this way. In pairwise classification, the order of the two input examples should not affect the classification result. To achieve this, particular kernels as well as the use of symmetric training sets in the framework of support vector machines were suggested. The paper discusses both approaches in a general way and establishes a strong connection between them. In addition, an efficient implementation is discussed which allows the training of several millions of pairs. The value of these contributions is confirmed by excellent results on the labeled faces in the wild benchmark.

66 citations


Network Information
Related Topics (5)
Markov chain
51.9K papers, 1.3M citations
81% related
Cluster analysis
146.5K papers, 2.9M citations
76% related
Deep learning
79.8K papers, 2.1M citations
75% related
Optimization problem
96.4K papers, 2.1M citations
74% related
Robustness (computer science)
94.7K papers, 1.6M citations
74% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20241
20231,305
20222,607
2021581
2020554
2019520