Knowledge-Based Support Vector Machine Classifiers

Abstract

Prior knowledge in the form of multiple polyhedral sets, each be-longing to one of two categories, is introduced into a reformulation of a linear support vector machine classifier. The resulting formu-lation 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 results show improvement in test set accuracy after the incorpo-ration of prior knowledge into ordinary, data-based linear support vector machine classifiers. One experiment also shows that a lin-ear classifier, based solely on prior knowledge, far outperforms the direct application of prior knowledge rules to classify data.