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NEVE: A Neuro-Evolutionary Ensemble for Adaptive Learning

  • Tatiana Escovedo
  • André Vargas Abs da Cruz
  • Marley Vellasco
  • Adriano Soares Koshiyama
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 412)

Abstract

This work describes the use of a quantum-inspired evolutionary algorithm (QIEA-R) to construct a weighted ensemble of neural network classifiers for adaptive learning in concept drift problems. The proposed algorithm, named NEVE (meaning Neuro-EVolutionary Ensemble), uses the QIEA-R to train the neural networks and also to determine the best weights for each classifier belonging to the ensemble when a new block of data arrives. After running eight simulations using two different datasets and performing two different analysis of the results, we show that NEVE is able to learn the data set and to quickly respond to any drifts on the underlying data, indicating that our model can be a good alternative to address concept drift problems. We also compare the results reached by our model with an existing algorithm, Learn++.NSE, in two different nonstationary scenarios.

Keywords

adaptive learning concept drift neuro-evolutionary ensemble quantum-inspired evolution 

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Copyright information

© IFIP International Federation for Information Processing 2013

Authors and Affiliations

  • Tatiana Escovedo
    • 1
  • André Vargas Abs da Cruz
    • 1
  • Marley Vellasco
    • 1
  • Adriano Soares Koshiyama
    • 1
  1. 1.Electrical Engineering DepartmentPontifical Catholic University of Rio de Janeiro (PUC-Rio)Rio de JaneiroBrazil

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