Abstract:
Pairwise Coupling is a statistical procedure designed to solve multi-class classification problems thought a combination of binary classifications. This thesis considers three different methods for pairwise coupling namely the Hastie and Tibshirani (1998) algorithm, the PKPD algorithm (Price et al., 1995) and voting rule (Knerr 1990; Friedman 1996). For each method, both linear discriminant analysis and logistic regression are considered to compute the pairwise probabilities. The three pairwise coupling methods are studied in detail and compared through simulations. Finally, real data are used to illustrate the methods."
