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Maximizing the Area under the ROC Curve with Decision Lists and Rule Sets
Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.ORCID-id: 0000-0001-8382-0300
2007 (Engelska)Ingår i: Proceedings of the 7th SIAM International Conference on Data Mining / [ed] C. Apte, B. Liu, S. Parthasarathy, D. Skillicorn, Society for Industrial and Applied Mathematics , 2007, s. 27-34Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Decision lists (or ordered rule sets) have two attractive properties compared to unordered rule sets: they require a simpler classi¯cation 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 observed gains in AUC for unordered rule sets compared to decision lists can be explained by that learning rules for all classes as well as combining multiple rules allow for examples to be ranked according to a more fine-grained scale compared to when applying rules in a fixed order and providing a default rule for one of the classes.

Ort, förlag, år, upplaga, sidor
Society for Industrial and Applied Mathematics , 2007. s. 27-34
Nationell ämneskategori
Datorseende och robotik (autonoma system) Diskret matematik Datavetenskap (datalogi)
Forskningsämne
Teknik
Identifikatorer
URN: urn:nbn:se:his:diva-2096ISI: 000289220200003Scopus ID: 2-s2.0-70449372884ISBN: 978-0-898716-30-6 (tryckt)OAI: oai:DiVA.org:his-2096DiVA, id: diva2:32372
Konferens
7th SIAM International Conference on Data Mining, Minneapolis, MN, 26 April 2007 through 28 April 2007
Tillgänglig från: 2008-05-30 Skapad: 2008-05-30 Senast uppdaterad: 2019-02-20Bibliografiskt granskad

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Boström, Henrik
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Institutionen för kommunikation och informationForskningscentrum för Informationsteknologi
Datorseende och robotik (autonoma system)Diskret matematikDatavetenskap (datalogi)

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