Distance-Based Decision Tree Algorithms for Label Ranking

dc.contributor.author Cláudio Rebelo Sá en
dc.contributor.author Rebelo,C en
dc.contributor.author Carlos Manuel Soares en
dc.contributor.author Knobbe,A en
dc.date.accessioned 2017-12-12T15:59:37Z
dc.date.available 2017-12-12T15:59:37Z
dc.date.issued 2015 en
dc.description.abstract The problem of Label Ranking is receiving increasing attention from several research communities. The algorithms that have developed/adapted to treat rankings as the target object follow two different approaches: distribution-based (e.g., using Mallows model) or correlation-based (e.g., using Spearman's rank correlation coefficient). Decision trees have been adapted for label ranking following both approaches. In this paper we evaluate an existing correlation-based approach and propose a new one, Entropy-based Ranking trees. We then compare and discuss the results with a distribution-based approach. The results clearly indicate that both approaches are competitive. en
dc.identifier.uri http://repositorio.inesctec.pt/handle/123456789/3924
dc.identifier.uri http://dx.doi.org/10.1007/978-3-319-23485-4_52 en
dc.language eng en
dc.relation 5527 en
dc.relation 5001 en
dc.rights info:eu-repo/semantics/openAccess en
dc.title Distance-Based Decision Tree Algorithms for Label Ranking en
dc.type conferenceObject en
dc.type Publication en
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
P-00G-SXT.pdf
Size:
292.65 KB
Format:
Adobe Portable Document Format
Description: