STRUCTURAL SYNTHESIS OF HYBRID NEURAL NETWORKS ENSEMBLES

Authors

  • V. M. Sineglazov National Aviation University, Kyiv
  • O. I. Chumachenko National Technical University of Ukraine “Ihor Sikorsky Kyiv Polytechnic Institute”
  • O. R. Bedukha National Aviation University, Kyiv

DOI:

https://doi.org/10.18372/1990-5548.57.13242

Keywords:

Hybrid neural networks ensembles, classifier, bootstrap training sample

Abstract

It is considered the structural synthesis of hybrid neural networks ensembles. It is chosen the ensemble topology as parallel structure with united layer. It is developed a hybrid algorithm for the problem solution which includes some algorithms preliminary choice of classifiers(modules of neural networks-hybrid neural networks, which consist of Kohonen, basic neural networks and bi-directional associative memory), creation the bootstrap training samples for every classifier, training these classifiers, optimal choice of necessity ones, determination of layer union weight coefficients, ensemble pruning. For the solution of optimal choice classifiers it is used two criteria: accuracy and variety.

Author Biographies

V. M. Sineglazov, National Aviation University, Kyiv

Aviation Computer-Integrated Complexes Department, Education & Scientific Institute of Information-Diagnostics Systems

Doctor of Engineering Science. Professor. Head of the Department

O. I. Chumachenko, National Technical University of Ukraine “Ihor Sikorsky Kyiv Polytechnic Institute”

Technical Cybernetic Department

Candidate of Science (Engineering). Assosiate Professor

O. R. Bedukha, National Aviation University, Kyiv

Aviation Computer-Integrated Complexes Department, Education & Scientific Institute of Information-Diagnostics Systems

Master

References

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Y. Zhang, S. Burer, and W. N. Street, “Ensemble pruning via semi-definite programming,” The Journal of Machine Learning Research. pp. 1315–1338, 2006.

Lu. Zhenyu, Wu Xindong, Zhu Xingquan, and Josh Bongard, “Ensamble Pruning via Individual Contribution Ordering,” Department of Computer Science University of Vermont, Burlington. NSW. 2007, pp. 635–745.

Gonzalo Martґınez-Muñoz, and Alberto Suárez, “Aggregation Ordering in Bagging,” in Proc. of the IASTED International Conference on Artificial Intelligence and Applications. Acta Press, 2004, pp. 258–263.

G. Martıґnez-Muñoz and A. Suárez, “Pruning in ordered bagging ensembles,” in Proc. of the 23rd International Conference on Machine Learning, 2006, pp. 609–616.

Anne M. P. Canuto, Marjory C. C. Abreu, Lucas de Melo Oliveira, João C. Xavier Jr., and Araken de M. Santos, “Investigating the influence of the choice of the ensemble members in accuracy and diversity of selection-based and fusion-based methods for ensembles,” Pattern Recognition Letters, 28, 2007, pp. 472–486.

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How to Cite

Sineglazov, V. M., Chumachenko, O. I., & Bedukha, O. R. (2018). STRUCTURAL SYNTHESIS OF HYBRID NEURAL NETWORKS ENSEMBLES. Electronics and Control Systems, 3(57), 83–87. https://doi.org/10.18372/1990-5548.57.13242

Issue

Vol. 3 No. 57 (2018)

Section

COMPUTER-AIDED DESIGN SYSTEMS

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