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.
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