Nonlinear separation of data via Mixed 0-1 Integer and Linear Programming

With extensive experiments on separation of two dimensional artificial datasets that are clean and noisy, we graphically illustrate the aforementioned advantages of the new MILP-based learning methodology. With experiments on real-life benchmark datasets from the UC Irvine Repository of machine learning databases, in comparison with the multisurface method and the support vector machines, we demonstrate the advantage of using and concurrently optimizing more than a single discriminant function for a robust separation of real-life data, hence the utility of the proposed methodology in supervised learning.