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Clustering and co-evolution to construct neural network ensembles: an experimental study.

Fernanda L Minku1, Teresa B Ludermir

  • 1School of Computer Science, The University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK. F.L.Minku@cs.bham.ac.uk

Neural Networks : the Official Journal of the International Neural Network Society
|April 2, 2008
PubMed
Summary
This summary is machine-generated.

This study presents Clustering and Co-evolution to Construct Neural Network Ensembles (CONE), a method that partitions input space for faster, more accurate neural network ensembles. CONE reduces computation time and improves performance through specialized networks.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computational Neuroscience

Background:

  • Neural network ensembles are powerful but computationally intensive.
  • Existing ensemble methods often lack efficient input space partitioning.
  • Evolutionary algorithms for training ensembles can be time-consuming.

Purpose of the Study:

  • To introduce a novel approach, Clustering and Co-evolution to Construct Neural Network Ensembles (CONE).
  • To reduce the computational cost and execution time of training neural network ensembles.
  • To maintain or improve ensemble accuracy by specializing networks through input space partitioning.

Main Methods:

  • Input space partitioning using a clustering method.
  • Constructing neural network ensembles (CONE) based on clustered data regions.
  • Utilizing co-evolutionary algorithms for ensemble training.
  • Evaluating performance on seven classification databases.

Main Results:

  • CONE significantly reduces execution time for training neural network ensembles.
  • Ensemble accuracy is maintained or improved compared to non-partitioned methods.
  • The clustering approach enables network specialization, enhancing divide-and-conquer capabilities.
  • Reduced number of nodes in individual networks contributes to efficiency.

Conclusions:

  • CONE offers an efficient strategy for constructing high-performing neural network ensembles.
  • The method is particularly beneficial when using evolutionary algorithms.
  • CONE facilitates system understanding and distributed implementation.
  • Empirical results validate the effectiveness of CONE in reducing training time without compromising accuracy.