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Rule Extraction from Support Vector Machines: Measuring the Explanation Capability Using the Area under the ROC Curve

9 years 11 months ago
Rule Extraction from Support Vector Machines: Measuring the Explanation Capability Using the Area under the ROC Curve
Recently, the area of rule extraction from support vector machines (SVMs) has been explored. One important indication of the success of a rule extraction method is the performance of extracted rules as compared to the original SVM. In this paper, we describe the use of the area under the receiver operating characteristics (ROC) curve (AUC) to assess the quality of rules extracted from an SVM. In particular, we directly compare AUC to the more commonly used measures of accuracy and fidelity and show that AUC is both a more reliable and meaningful measure to use.
Andrew P. Bradley, Nahla H. Barakat
Added 09 Nov 2009
Updated 09 Nov 2009
Type Conference
Year 2006
Where ICPR
Authors Andrew P. Bradley, Nahla H. Barakat
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