A Bayesian approach to Top-Scoring Pairs classification

Abstract

We extend the popular Top Scoring Pair (TSP) classification rule to a Bayesian setting, with the purpose of obtaining robust and effective classifiers for small-sample, high-dimensional data. We employ the Bradley-Terry model for rank data, and infer its parameters using a previously proposed Gibbs sampling algorithm. The parameters are then used to define a Bayesian TSP score, which is used to select the gene pairs to define the proposed Bayesian TSP classifiers. Accuracy of the proposed Bayesian classification rules is evaluated against those of the conventional TSP classifiers as well as other well-known machine learning methods, using a total of 12 gene-expression data sets. The results indicate that the Bayesian k-TSP classifier obtained the best overall average accuracy rate and the best accuracy rate over the majority of the individual data sets.