Towards simple, easy-to-understand, yet accurate classifiers

We design a method for weighting linear support vector machine classifiers or random hyperplanes, to obtain classifiers whose accuracy is comparable to the accuracy of a nonlinear support vector machine classifier, and whose results can be readily visualized. We conduct a simulation study to examine how our weighted linear classifiers behave in the presence of known structure. The results show that the weighted linear classifiers might perform well compared to the nonlinear support vector machine classifiers, while they are more readily interpretable than the nonlinear classifiers.