research-article

Decision tree and instance-based learning for label ranking

Authors:

Eyke HüllermeierAuthors Info & Claims

ICML '09: Proceedings of the 26th Annual International Conference on Machine Learning

Pages 161 - 168

https://doi.org/10.1145/1553374.1553395

Published: 14 June 2009 Publication History

Abstract

The label ranking problem consists of learning a model that maps instances to total orders over a finite set of predefined labels. This paper introduces new methods for label ranking that complement and improve upon existing approaches. More specifically, we propose extensions of two methods that have been used extensively for classification and regression so far, namely instance-based learning and decision tree induction. The unifying element of the two methods is a procedure for locally estimating predictive probability models for label rankings.

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Cited By

Shim ETewari ACernak TZimmerman P(2025)Recommending reaction conditions with label rankingChemical Science10.1039/D4SC06728BOnline publication date: 2025
https://doi.org/10.1039/D4SC06728B
D’Ambrosio AHeiser W(2025)A Recursive Partitioning Method for the Prediction of Preference Rankings Based Upon Kemeny DistancesPsychometrika10.1007/s11336-016-9505-181:3(774-794)Online publication date: 1-Jan-2025
https://doi.org/10.1007/s11336-016-9505-1
Rodrigo EAlfaro JAledo JGámez J(2024)Label ranking oblique treesKnowledge-Based Systems10.1016/j.knosys.2024.111882296(111882)Online publication date: Jul-2024
https://doi.org/10.1016/j.knosys.2024.111882
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Published In

cover image ACM Other conferences

ICML '09: Proceedings of the 26th Annual International Conference on Machine Learning

June 2009

1331 pages

ISBN:9781605585161

DOI:10.1145/1553374

General Chair:
Andrea Danyluk
Williams College
,
Program Chairs:
Léon Bottou
NEC Laboratories America
,
Michael Littman
Rutgers University

Copyright © 2009 Copyright 2009 by the author(s)/owner(s).

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NSF
Microsoft Research: Microsoft Research
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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 June 2009

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Microsoft Research

ICML '09: The 26th Annual International Conference on Machine Learning held in conjunction with the 2007 International Conference on Inductive Logic Programming

June 14 - 18, 2009

Quebec, Montreal, Canada

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Overall Acceptance Rate 140 of 548 submissions, 26%

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Cited By

Shim ETewari ACernak TZimmerman P(2025)Recommending reaction conditions with label rankingChemical Science10.1039/D4SC06728BOnline publication date: 2025
https://doi.org/10.1039/D4SC06728B
D’Ambrosio AHeiser W(2025)A Recursive Partitioning Method for the Prediction of Preference Rankings Based Upon Kemeny DistancesPsychometrika10.1007/s11336-016-9505-181:3(774-794)Online publication date: 1-Jan-2025
https://doi.org/10.1007/s11336-016-9505-1
Rodrigo EAlfaro JAledo JGámez J(2024)Label ranking oblique treesKnowledge-Based Systems10.1016/j.knosys.2024.111882296(111882)Online publication date: Jul-2024
https://doi.org/10.1016/j.knosys.2024.111882
Nakano FDulfer KVanhorebeek IWouters PVerbruggen SJoosten KGüiza Grandas FVens CVan den Berghe G(2024)Predicting adverse long-term neurocognitive outcomes after pediatric intensive care unit admissionComputer Methods and Programs in Biomedicine10.1016/j.cmpb.2024.108166(108166)Online publication date: Apr-2024
https://doi.org/10.1016/j.cmpb.2024.108166
Guo TZhang TWu HLi HQiao RSun X(2024)Multimodal Label Relevance Ranking via Reinforcement LearningComputer Vision – ECCV 202410.1007/978-3-031-72848-8_23(391-408)Online publication date: 29-Nov-2024
https://doi.org/10.1007/978-3-031-72848-8_23
Boehmer NFaliszewski PKraiczy SKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)Properties of the mallows model depending on the number of alternativesProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3618521(2689-2711)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.5555/3618408.3618521
Shih YKung Y(2023)Tree-Structured Model with Unbiased Variable Selection and Interaction Detection for Ranking DataMachine Learning and Knowledge Extraction10.3390/make50200275:2(448-459)Online publication date: 9-May-2023
https://doi.org/10.3390/make5020027
Rodrigo EAlfaro JAledo JGámez J(2023)Efficient ensembles of distance‐based label ranking treesExpert Systems10.1111/exsy.1352541:4Online publication date: 20-Dec-2023
https://doi.org/10.1111/exsy.13525
Elgharabawy APrasad MLin C(2023)Preference Neural NetworkIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2023.32687077:5(1362-1376)Online publication date: Oct-2023
https://doi.org/10.1109/TETCI.2023.3268707
Alfaro JAledo JGámez J(2023)Multi-dimensional Bayesian network classifiers for partial label rankingInternational Journal of Approximate Reasoning10.1016/j.ijar.2023.108950160(108950)Online publication date: Sep-2023
https://doi.org/10.1016/j.ijar.2023.108950
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