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 proceedingConference contributionAcademicpeer-review

    6 Citations (Scopus)


    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 languageEnglish
    Title of host publicationProgress in Artificial Intelligence
    Subtitle of host publication17th Portuguese Conference on Artificial Intelligence, EPIA 2015, Proceedings
    Number of pages10
    ISBN (Print)9783319234847
    Publication statusPublished - 1 Jan 2015
    Event17th Portuguese Conference on Artificial Intelligence, EPIA 2015 - Coimbra, Portugal
    Duration: 8 Sept 201511 Sept 2015
    Conference number: 17

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    Conference17th Portuguese Conference on Artificial Intelligence, EPIA 2015
    Abbreviated titleEPIA 2015

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