Classification by pairwise coupling

Classification by Pairwise Coupling

We discuss a strategy for polychotomous classification that involves estimating class probabilities for each pair of classes, and then coupling the estimates together. The coupling model is similar to the Bradley-Terry method for paired comparisons. We study the nature of the class probability estimates that arise, and examine the performance of the procedure in simulated datasets. The classifi...

Probability Estimates for Multi-class Classification by Pairwise Coupling

Pairwise coupling is a popular multi-class classification method that combines all comparisons for each pair of classes. This paper presents two approaches for obtaining class probabilities. Both methods can be reduced to linear systems and are easy to implement. We show conceptually and experimentally that the proposed approaches are more stable than the two existing popular methods: voting an...

Improved Pairwise Coupling Classification with Correcting Classifiers

Classi cation by Pairwise Coupling

We discuss a strategy for polychotomous classi cation that involves estimating class probabilities for each pair of classes and then cou pling the estimates together The coupling model is similar to the Bradley Terry method for paired comparisons We study the na ture of the class probability estimates that arise and examine the performance of the procedure in real and simulated datasets Clas si...

Disambiguating Authors by Pairwise Classification

Name ambiguity is a critical problem in many applications, in particular in the online bibliography systems, such as DBLP, ACM and CiteSeerx. By name disambiguation, we mean distinguishing persons with the same name. Despite many research works have been conducted, the problem is still not resolved and becomes even more serious, in particular with the popularity of Web 2.0. In this paper we foc...