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

    6 Citations (Scopus)

    Abstract

    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
    PublisherSpringer
    Pages155-169
    Number of pages15
    ISBN (Electronic)978-3-642-40897-7
    ISBN (Print)978-3-642-40896-0
    DOIs
    Publication statusPublished - 1 Jan 2013
    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
    PublisherSpringer
    Volume8140
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349
    NameLecture Notes in Artificial Intelligence
    PublisherSpringer

    Conference

    Conference16th International Conference on Discovery Science, DS 2013
    Abbreviated titleDS 2013
    CountrySingapore
    CitySingapore
    Period6/10/139/10/13

    Keywords

    • Association Rule
    • Shannon Entropy
    • Continuous Attribute
    • Discretization Method
    • Benchmark Dataset

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