Symbolic and Neural Learning Algorithms: An Experimental Comparison
Machine Learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
An integrated framework for risk profiling of breast cancer patients following surgery
Artificial Intelligence in Medicine
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
An empirical comparison of ID3 and back-propagation
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
A prototype integrated decision support system for breast cancer oncology
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Extracting rules from trained neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Unified forms for Kalman and finite impulse response filtering and smoothing
Automatica (Journal of IFAC)
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Support Vector Machines (SVM) are believed to be as powerful as Artificial Neural Networks (ANN) in modeling complex problems while avoiding some of the drawbacks of the latter such as local minimæ or reliance on architecture. However, a question that remains to be answered is whether SVM users may expect improvements in the interpretability of their models, namely by using rule extraction methods already available to ANN users. This study successfully applies the Orthogonal Search-based Rule Extraction algorithm (OSRE) to Support Vector Machines. The study evidences the portability of rules extracted using OSRE, showing that, in the case of SVM, extracted rules are as accurate and consistent as those from equivalent ANN models. Importantly, the study also shows that the OSRE method benefits from SVM specific characteristics, being able to extract less rules from SVM than from equivalent ANN models.