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

6 Citations (Scopus)

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
Publisher	Springer
Pages	525-534
Number of pages	10
ISBN (Print)	9783319234847
DOIs	https://doi.org/10.1007/978-3-319-23485-4_52
Publication status	Published - 1 Jan 2015
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 (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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

Access to Document

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

Cite this

de Sá, C. R., 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 (pp. 525-534). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9273). Springer. https://doi.org/10.1007/978-3-319-23485-4_52

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

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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.",

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

year = "2015",

month = jan,

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doi = "10.1007/978-3-319-23485-4_52",

language = "English",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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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 (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9273, Springer, 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. Springer, 2015. p. 525-534 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 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

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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.

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

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

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SN - 9783319234847

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Progress in Artificial Intelligence

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

Y2 - 8 September 2015 through 11 September 2015

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de Sá CR, Rebelo C, Soares C, Knobbe A. Distance-based decision tree algorithms for label ranking. In Progress in Artificial Intelligence: 17th Portuguese Conference on Artificial Intelligence, EPIA 2015, Proceedings. Springer. 2015. p. 525-534. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-23485-4_52

Distance-based decision tree algorithms for label ranking

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