Abstract
This paper presents a new approach to rule extraction from Support Vector Machines (SVMs). SVMs have been applied successfully in many areas with excellent generalization results; rule extraction can offer explanation capability to SVMs. We propose to approximate the SVM classification boundary by solving an optimization problem through sampling and querying followed by boundary searching, rule extraction and post-processing. A theorem and experimental results then indicate that the rules can be used to validate the SVM with high accuracy and very high fidelity.
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Ren, L., Garcez, A.d.A. (2009). Symbolic Knowledge Extraction from Support Vector Machines: A Geometric Approach. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03040-6_41
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DOI: https://doi.org/10.1007/978-3-642-03040-6_41
Publisher Name: Springer, Berlin, Heidelberg
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