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. Title: Multi-class minimax probability machine Authors: Dang, Tat-Dat; Nguyen, Ha-Nam Keywords: Genetic algorithms; Minimax probability machine; One-against-all; One-against-one Issue Date: 2009 Publisher: H:Đại học Quốc gia Hà Nội Abstract: This paper investigates the multi-class Minim...