Entropy-based discretization methods for ranking data

Cláudio Rebelo De Sá*, Carlos Soares, Arno Knobbe

*Corresponding author for this work

    Research output: Contribution to journalArticleAcademicpeer-review

    25 Citations (Scopus)

    Abstract

    Label Ranking (LR) problems are becoming increasingly important in Machine Learning. While there has been a significant amount of work on the development of learning algorithms for LR in recent years, there are not many pre-processing methods for LR. Some methods, like Naive Bayes for LR and APRIORI-LR, cannot handle real-valued data directly. Conventional discretization methods used in classification are not suitable for LR problems, due to the different target variable. In this work, we make an extensive analysis of the existing methods using simple approaches. We also propose a new method called EDiRa (Entropy-based Discretization for Ranking) for the discretization of ranking data. We illustrate the advantages of the method using synthetic data and also on several benchmark datasets. The results clearly indicate that the discretization is performing as expected and also improves the results and efficiency of the learning algorithms.

    Original languageEnglish
    Pages (from-to)921-936
    Number of pages16
    JournalInformation sciences
    Volume329
    DOIs
    Publication statusPublished - 1 Feb 2016

    Keywords

    • Association Rule Mining
    • Discretization
    • Label ranking
    • Minimum description length

    Fingerprint Dive into the research topics of 'Entropy-based discretization methods for ranking data'. Together they form a unique fingerprint.

    Cite this