Abstract

Prior knowledge in the form of multiple polyhedral sets, each belonging to one of two categories, is introduced into a reformulation of a linear support vector machine classifier. The resulting formulation leads to a linear program that can be solved efficiently. Real world examples, from DNA sequencing and breast cancer prognosis, demonstrate the effectiveness of the proposed method. Numerical resuks show improvement in test set accuracy after the incorporation of prior knowledge into ordinary data-based linear support vector machine classifiers. One experiment also shows that a linear classifier, based solely on prior knowledge, far outperforms the direct application of the prior knowledge rules to classify new examples.

Citations