Multi-class Minimax Probability Machine

This paper investigates the multi-class Minimax Probability Machine (MPM). MPM constructs a binary classifier that provides a worst-case bound on the probability of misclassification of future data points, based on reliable estimates of means and covariance matrices of the classes from the training data points. We propose a method to adapt MPM to multi-class datasets using the one-against-all strategy. And then we introduce an optimal kernel for MPM for each specific dataset found by Genetic Algorithms (GA) [1]. The proposed method was evaluated on stomach cancer data. The obtained results are better and more stable than for using a single kernel.