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|dc.title||Rule extraction from support vector machines: An overview of issues and application in credit scoring|
|dc.identifier.citation||Martens, D.,Huysmans, J.,Setiono, R.,Vanthienen, J.,Baesens, B. (2008). Rule extraction from support vector machines: An overview of issues and application in credit scoring. Studies in Computational Intelligence 80 : 33-63. ScholarBank@NUS Repository. <a href="https://doi.org/10.1007/978-3-540-75390-2_2" target="_blank">https://doi.org/10.1007/978-3-540-75390-2_2</a>|
|dc.description.abstract||Innovative storage technology and the rising popularity of the Internet have generated an ever-growing amount of data. In this vast amount of data much valuable knowledge is available, yet it is hidden. The Support Vector Machine (SVM) is a state-of-the-art classification technique that generally provides accurate models, as it is able to capture non-linearities in the data. However, this strength is also its main weakness, as the generated non-linear models are typically regarded as incomprehensible black-box models. By extracting rules that mimic the black box as closely as possible, we can provide some insight into the logics of the SVM model. This explanation capability is of crucial importance in any domain where the model needs to be validated before being implemented, such as in credit scoring (loan default prediction) and medical diagnosis. If the SVM is regarded as the current state-of-the-art, SVM rule extraction can be the state-of-the-art of the (near) future. This chapter provides an overview of recently proposed SVM rule extraction techniques, complemented with the pedagogical Artificial Neural Network (ANN) rule extraction techniques which are also suitable for SVMs. Issues related to this topic are the different rule outputs and corresponding rule expressiveness; the focus on high dimensional data as SVM models typically perform well on such data; and the requirement that the extracted rules are in line with existing domain knowledge. These issues are explained and further illustrated with a credit scoring case, where we extract a Trepan tree and a RIPPER rule set from the generated SVM model. The benefit of decision tables in a rule extraction context is also demonstrated. Finally, some interesting alternatives for SVM rule extraction are listed. © 2008 Springer-Verlag Berlin Heidelberg.|
|dc.description.sourcetitle||Studies in Computational Intelligence|
|Appears in Collections:||Staff Publications|
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