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Classification of Binary Imbalanced Data Using A Bayesian Ensemble of Bayesian Neural Networks

  • Marcelino Lázaro
  • Francisco Herrera
  • Aníbal R. Figueiras-Vidal
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 517)

Abstract

This paper presents a new method to deal with classification of imbalanced data. A Bayesian ensemble of neural network classifiers is proposed. Several individual neural classifiers are trained to minimize a Bayesian cost function with different decision costs, thus working at different points of the Receiver Operating Characteristic (ROC). Decisions of the set of individual neural classifiers are fused using a Bayesian rule that introduces a “balancing” parameter allowing to compensate the imbalance of available data.

Keywords

Imbalanced data Classification Neural networks Bayes risk 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Marcelino Lázaro
    • 1
  • Francisco Herrera
    • 2
  • Aníbal R. Figueiras-Vidal
    • 1
  1. 1.Department of Signal Theory and CommunicationsCarlos III University of MadridLeganésSpain
  2. 2.Department of Computer Science and Artificial IntelligenceUniversity of GranadaGranadaSpain

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