The Effect of Domain Knowledge on Rule Extraction from Support Vector Machines

Authors:
Nahla Barakat;Andrew P. Bradley
Affiliations:
German University of Technology in Oman,;School of Information Technology and Electrical Engineering (ITEE), The University of Queensland, St Lucia, Australia 4072
Venue:
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Year:
2009

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Abstract

Prior knowledge about a problem domain can be utilized to bias Support Vector Machines (SVMs) towards learning better hypothesis functions. To this end, a number of methods have been proposed that demonstrate improved generalization performance after the application of domain knowledge; especially in the case of scarce training data. In this paper, we propose an extension to the virtual support vectors (VSVs) technique where only a subset of the support vectors (SVs) is utilized. Unlike previous methods, the purpose here is to compensate for noise and uncertainty in the training data. Furthermore, we investigate the effect of domain knowledge not only on the quality of the SVM model, but also on rules extracted from it; hence the learned pattern by the SVM. Results on five benchmark and one real life data sets show that domain knowledge can significantly improve both the quality of the SVM and the rules extracted from it.