Evolving Multi-label Classification Rules with Gene Expression Programming: A Preliminary Study

  • José Luis Ávila-Jiménez
  • Eva Gibaja
  • Sebastián Ventura
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6077)


The present work expounds a preliminary work of a genetic programming algorithm to deal with multi-label classification problems. The algorithm uses Gene Expression Programming and codifies a classification rule into each individual. A niching technique assures diversity in the population. The final classifier is made up by a set of rules for each label that determines if a pattern belongs or not to the label. The proposal have been tested over several domains and compared with other multi-label algorithms and the results shows that it is specially suitable to handle with nominal data sets.


Gene Expression Programming Expression Tree Binary Relevance Genetic Programming Model Terminal Element 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • José Luis Ávila-Jiménez
    • 1
  • Eva Gibaja
    • 1
  • Sebastián Ventura
    • 1
  1. 1.Department of Computer Sciences and Numerical AnalysisUniversity of Córdoba 

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