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2007
SIAM

Maximizing the Area under the ROC Curve with Decision Lists and Rule Sets

9 years 3 months ago
Maximizing the Area under the ROC Curve with Decision Lists and Rule Sets
Decision lists (or ordered rule sets) have two attractive properties compared to unordered rule sets: they require a simpler classification procedure and they allow for a more compact representation. However, it is an open question what effect these properties have on the area under the ROC curve (AUC). Two ways of forming decision lists are considered in this study: by generating a sequence of rules, with a default rule for one of the classes, and by imposing an order upon rules that have been generated for all classes. An empirical investigation shows that the latter method gives a significantly higher AUC than the former, demonstrating that the compactness obtained by using one of the classes as a default is indeed associated with a cost. Furthermore, by using all applicable rules rather than the first in an ordered set, an even further significant improvement in AUC is obtained, demonstrating that the simple classification procedure is also associated with a cost. The observ...
Henrik Boström
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2007
Where SDM
Authors Henrik Boström
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