Knowledge-Based Support Vector Machine Classi ers

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 classi er. The resulting formulation leads to a linear program that can be solved e ciently. Real world examples, from DNA sequencing and breast cancer prognosis, demonstrate the e ectiveness of the proposed method. Numerical results show improvement in test set accuracy after the incorporation of prior knowledge into ordinary, data-based linear support vector machine classi ers. One experiment also shows that a linear classi er, based solely on prior knowledge, far outperforms the direct application of prior knowledge rules to classify data.