G. Fung, O. Mangasarian & J. Shavlik (2001).
Knowledge-based Support Vector Machine Classifiers. Data Mining Institute, University of Wisconsin, DMI TR 01-09.
(A shorter version of this paper appears in Advances in Neural Information Processing [NIPS], 2002)
This publication is available in PDF and available in postscript.
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 results show improvement in the 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 prior knowledge rules to classify data.
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