Ensemble learning using multi-objective evolutionary algorithms


Research output: Journal PublicationsJournal Article (refereed)peer-review

136 Citations (Scopus)


Multi-objective evolutionary algorithms for the construction of neural ensembles is a relatively new area of research. We recently proposed an ensemble learning algorithm called DIVACE (DIVerse and ACcurate Ensemble learning algorithm). It was shown that DIVACE tries to find an optimal trade-off between diversity and accuracy as it searches for an ensemble for some particular pattern recognition task by treating these two objectives explicitly separately. A detailed discussion of DIVACE together with further experimental studies form the essence of this paper. A new diversity measure which we call Pairwise Failure Crediting (PFC) is proposed. This measure forms one of the two evolutionary pressures being exerted explicitly in DIVACE. Experiments with this diversity measure as well as comparisons with previously studied approaches are hence considered. Detailed analysis of the results show that DIVACE, as a concept, has promise. © Springer 2006.
Original languageEnglish
Pages (from-to)417-445
Number of pages29
JournalJournal of Mathematical Modelling and Algorithms
Issue number4
Early online date8 Mar 2006
Publication statusPublished - Dec 2006
Externally publishedYes


  • Diversity
  • Ensemble learning
  • Multi-objective learning
  • Neural networks
  • Neuroevolution


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