Transductive minimax probability machine

Abstract

The Minimax Probability Machine (MPM) is an elegant machine learning algorithm for inductive learning. It learns a classifier that minimizes an upper bound on its own generalization error. In this paper, we extend its celebrated inductive formulation to an equally elegant transductive learning algorithm. In the transductive setting, the label assignment of a test set is already optimized during training. This optimization problem is an intractable mixed-integer programming. Thus, we provide an efficient label-switching approach to solve it approximately. The resulting method scales naturally to large data sets and is very efficient to run. In comparison with nine competitive algorithms on eleven data sets, we show that the proposed Transductive MPM (TMPM) almost outperforms all the other algorithms in both accuracy and speed. © 2014 Springer-Verlag.