Please use this identifier to cite or link to this item: http://repositorio.inesctec.pt/handle/123456789/3924
Title: Distance-Based Decision Tree Algorithms for Label Ranking
Authors: Cláudio Rebelo Sá
Rebelo,C
Carlos Manuel Soares
Knobbe,A
Issue Date: 2015
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.
URI: http://repositorio.inesctec.pt/handle/123456789/3924
http://dx.doi.org/10.1007/978-3-319-23485-4_52
metadata.dc.type: conferenceObject
Publication
Appears in Collections:CESE - Articles in International Conferences

Files in This Item:
File Description SizeFormat 
P-00G-SXT.pdf292.65 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.