Multi-interval discretization of continuous attributes for label ranking

Cláudio Rebelo de Sá, Carlos Soares, Arno Knobbe, Paulo Azevedo, Alípio Mário Jorge

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

12 Citations (Scopus)


Label Ranking (LR) problems, such as predicting rankings of financial analysts, are becoming increasingly important in data mining. While there has been a significant amount of work on the development of learning algorithms for LR in recent years, pre-processing methods for LR are still very scarce. However, some methods, like Naive Bayes for LR and APRIORI-LR, cannot deal with real-valued data directly. As a make-shift solution, one could consider conventional discretization methods used in classification, by simply treating each unique ranking as a separate class. In this paper, we show that such an approach has several disadvantages. As an alternative, we propose an adaptation of an existing method, MDLP, specifically for LR problems. We illustrate the advantages of the new method using synthetic data. Additionally, we present results obtained on several benchmark datasets. The results clearly indicate that the discretization is performing as expected and in some cases improves the results of the learning algorithms.

Original languageEnglish
Title of host publicationDiscovery Science
Subtitle of host publication16th International Conference, DS 2013, Singapore, October 6-9, 2013. Proceedings
EditorsJohannes Fürnkranz, Eyke Hüllermeier, Tomoyuki Higuchi
Place of PublicationBerlin, Heidelberg
Number of pages15
ISBN (Electronic)978-3-642-40897-7
ISBN (Print)978-3-642-40896-0
Publication statusPublished - 1 Jan 2013
Externally publishedYes
Event16th International Conference on Discovery Science, DS 2013 - Singapore, Singapore
Duration: 6 Oct 20139 Oct 2013
Conference number: 16

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349
NameLecture Notes in Artificial Intelligence


Conference16th International Conference on Discovery Science, DS 2013
Abbreviated titleDS 2013


  • Association Rule
  • Shannon Entropy
  • Continuous Attribute
  • Discretization Method
  • Benchmark Dataset
  • n/a OA procedure


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