Pairwise comparison

About: Pairwise comparison is a research topic. Over the lifetime, 6804 publications have been published within this topic receiving 174081 citations.

Causal Cognitive Mapping in the Organizational Strategy Field: A Comparison of Alternative Elicitation Procedures:

Abstract: The present study evaluates two alternative causal cognitive mapping procedures that exemplify key differences among a number of direct elicitation techniques currently in use in the organizational strategy field: pairwise evaluation of causal relationships and a freeh and approach. The pairwise technique yielded relatively elaborate maps, but participants found the task more difficult, less engaging, and less representative than the freeh and approach. Implications for the choice of procedures in interventionist and research contexts are considered.

Large Scale Max-Margin Multi-Label Classification with Priors

Abstract: We propose a max-margin formulation for the multi-label classification problem where the goal is to tag a data point with a set of pre-specified labels. Given a set of L labels, a data point can be tagged with any of the 2L possible subsets. The main challenge therefore lies in optimising over this exponentially large label space subject to label correlations. Existing solutions take either of two approaches. The first assumes, a priori, that there are no label correlations and independently trains a classifier for each label (as is done in the 1-vs-All heuristic). This reduces the problem complexity from exponential to linear and such methods can scale to large problems. The second approach explicitly models correlations by pairwise label interactions. However, the complexity remains exponential unless one assumes that label correlations are sparse. Furthermore, the learnt correlations reflect the training set biases. We take a middle approach that assumes labels are correlated but does not incorporate pairwise label terms in the prediction function. We show that the complexity can still be reduced from exponential to linear while modelling dense pairwise label correlations. By incorporating correlation priors we can overcome training set biases and improve prediction accuracy. We provide a principled interpretation of the 1-vs-All method and show that it arises as a special case of our formulation. We also develop efficient optimisation algorithms that can be orders of magnitude faster than the state-of-the-art.

Efficient pairwise multilabel classification for large-scale problems in the legal domain

Abstract: In this paper we applied multilabel classification algorithms to the EUR-Lex database of legal documents of the European Union. On this document collection, we studied three different multilabel classification problems, the largest being the categorization into the EUROVOC concept hierarchy with almost 4000 classes. We evaluated three algorithms: (i) the binary relevance approach which independently trains one classifier per label; (ii) the multiclass multilabel perceptron algorithm, which respects dependencies between the base classifiers; and (iii) the multilabel pairwise perceptron algorithm, which trains one classifier for each pair of labels. All algorithms use the simple but very efficient perceptron algorithm as the underlying classifier, which makes them very suitable for large-scale multilabel classification problems. The main challenge we had to face was that the almost 8,000,000 perceptrons that had to be trained in the pairwise setting could no longer be stored in memory. We solve this problem by resorting to the dual representation of the perceptron, which makes the pairwise approach feasible for problems of this size. The results on the EUR-Lex database confirm the good predictive performance of the pairwise approach and demonstrates the feasibility of this approach for large-scale tasks.

Composite likelihood estimation for the Brown–Resnick process

Abstract: SUMMARY Genton et al. (2011) investigated the gain in efficiency when triplewise, rather than pairwise, likelihood is used to fit the popular Smith max-stable model for spatial extremes. We generalize their results to the Brown–Resnick model and show that the efficiency gain is substantial only for very smooth processes, which are generally unrealistic in applications.

Information Revelation in a Market with Pairwise Meetings

Abstract: The paper presents a simple pairwise meetings model of trade. The new feature is that agents have asymmetric information about the true state of the world. The focus is on the transmission of the information through the process of trade. The qualitative question is : to what extent is the information revealed to uninformed agents through the trading process, when the market is in some sense frictionless? In particular ; does the decentralized process give rise to full revelation results as derived by the literature on rational expectations for centralized and competitive environments? In the context of the model of this paper, it turns out that the information is not fully revealed to uninformed agents, even when the market is in some sense approximately frictionless.