Measuring diversity and accuracy in ANN ensembles

M. Paz Sesmero, Juan Manuel Alonso-Weber, Alessandro Giuliani, Giuliano Armano, Araceli Sanchis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Performance of classifier ensembles depends on the precision and on the diversity of the members of the ensemble. In this paper we present an experimental study in which the relationship between the accuracy of the ensemble and both the diversity and the accuracy of base learners is analyzed. We conduct experiments on 8 different ANN ensembles and on 5 multiclass data sets. Experimental results show that a high diversity degree among the base learners does not always imply a high accuracy in the ensemble.

Original languageEnglish
Title of host publicationAdvances in Artificial Intelligence - 18th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2018, Proceedings
EditorsAntonio Gonzalez, Alicia Troncoso, Francisco Herrera, Sergio Damas, Rosana Montes, Sergio Alonso, Oscar Cordon
PublisherSpringer Verlag
Pages108-117
Number of pages10
ISBN (Print)9783030003739
DOIs
Publication statusPublished - Jan 1 2018
Event18th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2018 - Granada, Spain
Duration: Oct 23 2018Oct 26 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11160 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2018
CountrySpain
CityGranada
Period10/23/1810/26/18

Keywords

  • Accuracy
  • ANN
  • Diversity
  • Ensemble of classifiers

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Measuring diversity and accuracy in ANN ensembles'. Together they form a unique fingerprint.

  • Cite this

    Sesmero, M. P., Alonso-Weber, J. M., Giuliani, A., Armano, G., & Sanchis, A. (2018). Measuring diversity and accuracy in ANN ensembles. In A. Gonzalez, A. Troncoso, F. Herrera, S. Damas, R. Montes, S. Alonso, & O. Cordon (Eds.), Advances in Artificial Intelligence - 18th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2018, Proceedings (pp. 108-117). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11160 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-030-00374-6_11