Get access

An Extensive Evaluation of Decision Tree–Based Hierarchical Multilabel Classification Methods and Performance Measures

Authors

  • Ricardo Cerri,

    Corresponding author
    1. Departamento de Ciências de Computação, Universidade de São Paulo, São Carlos, SP, Brazil
    • Address correspondence to Ricardo Cerri, Departamento de Ciências de Computação, Universidade de São Paulo, Campus de São Carlos, Av. Trabalhador São-carlense, 400, Centro, 13560-970, São Carlos, SP, Brazil; e-mail: cerri@icmc.usp.br

    Search for more papers by this author
  • Gisele L. Pappa,

    1. Departamento de Ciências da Computação, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
    Search for more papers by this author
  • André Carlos P.L.F. Carvalho,

    1. Departamento de Ciências de Computação, Universidade de São Paulo, São Carlos, SP, Brazil
    Search for more papers by this author
  • Alex A. Freitas

    1. School of Computing, University of Kent, Canterbury, Kent, UK
    Search for more papers by this author

Abstract

Hierarchical multilabel classification is a complex classification problem where an instance can be assigned to more than one class simultaneously, and these classes are hierarchically organized with superclasses and subclasses, that is, an instance can be classified as belonging to more than one path in the hierarchical structure. This article experimentally analyses the behavior of different decision tree–based hierarchical multilabel classification methods based on the local and global classification approaches. The approaches are compared using distinct hierarchy-based and distance-based evaluation measures, when they are applied to a variation of real multilabel and hierarchical datasets' characteristics. Also, the different evaluation measures investigated are compared according to their degrees of consistency, discriminancy, and indifferency. As a result of the experimental analysis, we recommend the use of the global classification approach and suggest the use of the Hierarchical Precision and Hierarchical Recall evaluation measures.

Ancillary