Distance-based decision tree algorithms for label ranking

Cláudio Rebelo de Sá; Carla Rebelo; Carlos Soares; Arno Knobbe

doi:10.1007/978-3-319-23485-4_52

Distance-based decision tree algorithms for label ranking

Cláudio Rebelo de Sá^*, Carla Rebelo, Carlos Soares, Arno Knobbe

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

8 Citations (Scopus)

5 Downloads (Pure)

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.

Original language	English
Title of host publication	Progress in Artificial Intelligence
Subtitle of host publication	17th Portuguese Conference on Artificial Intelligence, EPIA 2015, Proceedings
Place of Publication	Cham
Publisher	Springer
Pages	525-534
Number of pages	10
ISBN (Electronic)	978-3-319-23485-4
ISBN (Print)	978-3-319-23484-7
DOIs	https://doi.org/10.1007/978-3-319-23485-4_52
Publication status	Published - 1 Jan 2015
Externally published	Yes
Event	17th Portuguese Conference on Artificial Intelligence, EPIA 2015 - Coimbra, Portugal Duration: 8 Sept 2015 → 11 Sept 2015 Conference number: 17

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	9273
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	17th Portuguese Conference on Artificial Intelligence, EPIA 2015
Abbreviated title	EPIA 2015
Country/Territory	Portugal
City	Coimbra
Period	8/09/15 → 11/09/15

Keywords

n/a OA procedure

Access to Document

10.1007/978-3-319-23485-4_52

Cite this

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title = "Distance-based decision tree algorithms for label ranking",

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{\textquoteright}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.",

keywords = "n/a OA procedure",

author = "\{de S{\'a}\}, \{Cl{\'a}udio Rebelo\} and Carla Rebelo and Carlos Soares and Arno Knobbe",

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language = "English",

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series = "Lecture Notes in Computer Science",

publisher = "Springer",

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booktitle = "Progress in Artificial Intelligence",

address = "Germany",

note = "17th Portuguese Conference on Artificial Intelligence, EPIA 2015, EPIA 2015 ; Conference date: 08-09-2015 Through 11-09-2015",

}

de Sá, CR, Rebelo, C, Soares, C & Knobbe, A 2015, Distance-based decision tree algorithms for label ranking. in Progress in Artificial Intelligence: 17th Portuguese Conference on Artificial Intelligence, EPIA 2015, Proceedings. Lecture Notes in Computer Science, vol. 9273, Springer, Cham, pp. 525-534, 17th Portuguese Conference on Artificial Intelligence, EPIA 2015, Coimbra, Portugal, 8/09/15. https://doi.org/10.1007/978-3-319-23485-4_52

Distance-based decision tree algorithms for label ranking. / de Sá, Cláudio Rebelo; Rebelo, Carla; Soares, Carlos et al.
Progress in Artificial Intelligence: 17th Portuguese Conference on Artificial Intelligence, EPIA 2015, Proceedings. Cham: Springer, 2015. p. 525-534 (Lecture Notes in Computer Science; Vol. 9273).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Distance-based decision tree algorithms for label ranking

AU - de Sá, Cláudio Rebelo

AU - Rebelo, Carla

AU - Soares, Carlos

AU - Knobbe, Arno

N1 - Conference code: 17

PY - 2015/1/1

Y1 - 2015/1/1

N2 - 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.

AB - 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.

KW - n/a OA procedure

UR - https://www.scopus.com/pages/publications/84945955147

U2 - 10.1007/978-3-319-23485-4_52

DO - 10.1007/978-3-319-23485-4_52

M3 - Conference contribution

AN - SCOPUS:84945955147

SN - 978-3-319-23484-7

T3 - Lecture Notes in Computer Science

SP - 525

EP - 534

BT - Progress in Artificial Intelligence

PB - Springer

CY - Cham

T2 - 17th Portuguese Conference on Artificial Intelligence, EPIA 2015

Y2 - 8 September 2015 through 11 September 2015

ER -

Distance-based decision tree algorithms for label ranking

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