Show simple item record  

dc.contributor.authorFrank, Eibeen_NZ
dc.contributor.authorMayo, Michaelen_NZ
dc.contributor.authorKramer, Stefanen_NZ
dc.coverage.spatialSalamanca, Spainen_NZ
dc.date.accessioned2015-06-10T23:56:51Z
dc.date.available2015en_NZ
dc.date.available2015-06-10T23:56:51Z
dc.date.issued2015en_NZ
dc.identifier.citationFrank, E., Mayo, M., & Kramer, S. (2015). Alternating model trees. In Proc 30th ACM Symposium on Applied Computing, Data Mining Track. Salamanca, Spain: ACM Press.en
dc.identifier.urihttps://hdl.handle.net/10289/9398
dc.description.abstractModel tree induction is a popular method for tackling regression problems requiring interpretable models. Model trees are decision trees with multiple linear regression models at the leaf nodes. In this paper, we propose a method for growing alternating model trees, a form of option tree for regression problems. The motivation is that alternating decision trees achieve high accuracy in classification problems because they represent an ensemble classifier as a single tree structure. As in alternating decision trees for classifi-cation, our alternating model trees for regression contain splitter and prediction nodes, but we use simple linear regression functions as opposed to constant predictors at the prediction nodes. Moreover, additive regression using forward stagewise modeling is applied to grow the tree rather than a boosting algorithm. The size of the tree is determined using cross-validation. Our empirical results show that alternating model trees achieve significantly lower squared error than standard model trees on several regression datasets.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherACM Pressen_NZ
dc.rightsThis is an author’s accepted version of an article in Proceedings of 38th Annual ACM SIGIR conference, Santiago de Chile, Chile. © 2015 ACM.
dc.sourceSAC 2015en_NZ
dc.subjectcomputer science
dc.subjectregression
dc.subjectalternating model trees
dc.titleAlternating model treesen_NZ
dc.typeConference Contribution
dc.identifier.doi10.1145/2695664.2695848en_NZ
dc.relation.isPartOfProc 30th ACM Symposium on Applied Computing, Data Mining Tracken_NZ
pubs.begin-page871en_NZ
pubs.elements-id120712
pubs.end-page878en_NZ
pubs.finish-date2015-04-17en_NZ
pubs.organisational-group/Waikato
pubs.organisational-group/Waikato/FCMS
pubs.organisational-group/Waikato/FCMS/Computer Science
pubs.start-date2015-04-13en_NZ
pubs.volume13-17-April-2015en_NZ


Files in this item

This item appears in the following Collection(s)

Show simple item record