Sökning: onr:"swepub:oai:DiVA.org:his-2096" > Maximizing the Area...
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000 | 03571naa a2200397 4500 | |
001 | oai:DiVA.org:his-2096 | |
003 | SwePub | |
008 | 080530s2007 | |||||||||||000 ||eng| | |
009 | oai:DiVA.org:kth-221459 | |
024 | 7 | a https://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-20962 URI |
024 | 7 | a https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-2214592 URI |
040 | a (SwePub)hisd (SwePub)kth | |
041 | a engb eng | |
042 | 9 SwePub | |
072 | 7 | a ref2 swepub-contenttype |
072 | 7 | a kon2 swepub-publicationtype |
100 | 1 | a Boström, Henriku Högskolan i Skövde,Institutionen för kommunikation och information,Forskningscentrum för Informationsteknologi,Högskolan i Skövde, Institutionen för kommunikation och information4 aut0 (Swepub:kth)u1r0rr47 |
245 | 1 0 | a Maximizing the Area under the ROC Curve with Decision Lists and Rule Sets |
264 | 1 | b Society for Industrial and Applied Mathematics,c 2007 |
338 | a print2 rdacarrier | |
500 | a QC 20180119 | |
520 | a 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. | |
650 | 7 | a NATURVETENSKAPx Data- och informationsvetenskapx Datorseende och robotik0 (SwePub)102072 hsv//swe |
650 | 7 | a NATURAL SCIENCESx Computer and Information Sciencesx Computer Vision and Robotics0 (SwePub)102072 hsv//eng |
650 | 7 | a NATURVETENSKAPx Matematikx Diskret matematik0 (SwePub)101042 hsv//swe |
650 | 7 | a NATURAL SCIENCESx Mathematicsx Discrete Mathematics0 (SwePub)101042 hsv//eng |
650 | 7 | a NATURVETENSKAPx Data- och informationsvetenskapx Datavetenskap0 (SwePub)102012 hsv//swe |
650 | 7 | a NATURAL SCIENCESx Computer and Information Sciencesx Computer Sciences0 (SwePub)102012 hsv//eng |
650 | 7 | a NATURVETENSKAPx Data- och informationsvetenskap0 (SwePub)1022 hsv//swe |
650 | 7 | a NATURAL SCIENCESx Computer and Information Sciences0 (SwePub)1022 hsv//eng |
653 | a Technology | |
653 | a Teknik | |
710 | 2 | a Högskolan i Skövdeb Institutionen för kommunikation och information4 org |
773 | 0 | t Proceedings of the 7th SIAM International Conference on Data Miningd : Society for Industrial and Applied Mathematicsg , s. 27-34q <27-34z 9780898716306 |
856 | 4 8 | u https://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-2096 |
856 | 4 8 | u https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-221459 |
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