A Stratified Sampling Algorithm for Artificial Neural Networks

  • Danilo Douradinho Fernandes
  • Gustavo Ravanhani Matuck
  • Denis Avila Montini
  • Luiz Alberto Vieira Dias
  • Alessandra Avila Montini
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 738)


Artificial Neural Networks (ANN) MultiLayer Perceptron (MLP) are widely applied in a variety market segments to handle with real complex problems. The ability to deal with tasks in real time is essential in an environment that uses large volume do information available. In each new project, a decision-making system using ANN with time reduction and data processing is a key issue to test various learning algorithms; containing a variety of parameters when using this technology. From this starting point, the MLPs used data collected from a specific phenomenon and, based on statistical estimators, applied a data extraction algorithm for stratified sampling, aiming to reduce the time of ANN processing. In this context, this work proposes a Stratified Sampling algorithm (SSA), which was developed to minimize processing MLPs time without losing coverage and assertiveness, when comparing with training conducted on a population database. The case study consisted of a ANN performance influence with a population database and with its sample data obtained by the SSA model. This procedure with the RNAs aimed to evaluate the following properties: (1) meet the pre-established criteria of reliability of the model; (2) have a computer-automated procedure; (3) sort and select records more correlated, and (4) maintain sampling results within a track of assertiveness of total results obtained. From the realization of this case study, it was possible to identify the following gains made by the (1) reduction of ANN processing time by providing: (2) optimization of processing time; (3) automatic network selection; and (4) automatic parameters selection for training algorithms.


Artificial Neural Network Stratified Sampling Algorithm Multilayer Perceptron 


Acknowledgements and General Considerations

The Research Group on Software Engineering thanks for institutions and research groups like the Brazilian Aeronautics Technological Institute (ITA) and Administration Institute Foundation (FIA), for the contributions, support and cooperation.


  1. 1.
    S. Haykin, Neural Networks and Learning Machines (Pearson Education, Inc., Upper Saddle River, NJ, 2009)Google Scholar
  2. 2.
    D.A. Montini, G.R. Matuck, A.M. da Cunha, L.A.V. Dias, A sampling diagnostics model for neural system training optimization, in 2013 10th International Conference on Information Technology: New Generations (Las Vegas, 2013)Google Scholar
  3. 3.
    G.R. Matuck, J.R. Barbosa, C. Bringhenti, I. Lima, Multiple faults detection of gas turbine by MLP neural network, in ASME Turbo Expo 2009, Power for Land, Sea, and Air (Orlando, 2009)Google Scholar
  4. 4.
    S. Russell, P. Norvig, Artificial Intelligence. A Modern Approach (Prentice Hall, 2003)Google Scholar
  5. 5.
    D.A. Montini, P.M. Tasinaffo, A.A. Montini, L.A.V. Dias, A.M. Cunha, Um Meta-Algoritmo para Otimização de Planejamento em Linha de Produção de software, in VIII International Conference on Engineering and Computer Education—ICECE 2013 (Luanda, 2003) Google Scholar
  6. 6.
    The MathWorks, Inc, MATLAB Software [Online], https://www. Accessed 06 Apr 2017
  7. 7.
    W.J. Stevenson, B.J. Isselhardt, Study Guide to Accompany Business Statistics: Concepts and Applications, 2nd edn (Harper & Row, 1978)Google Scholar
  8. 8.
    C.M. Bishop, Neural Networks for Pattern Recognition (Clarendon Press, Oxford, 1995)zbMATHGoogle Scholar
  9. 9.
    D.A. Montini, D. Battaglia, G.R. Matuck, A chi-square methodology applied in deviations control of project plan to support the RIMAM model, in 2014 11th International Conference on Information Technology: New Generations (Las Vegas, 2014)Google Scholar
  10. 10.
    Z. Yue, Z. Songzheng, L. Tianshi, Bayesian regularization BP Neural Network model for predicting oil-gas drilling cost, in 2011 International Conference on Business Management and Electronic Information (Guangzhou, China, 2011)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Danilo Douradinho Fernandes
    • 1
  • Gustavo Ravanhani Matuck
    • 2
  • Denis Avila Montini
    • 2
  • Luiz Alberto Vieira Dias
    • 2
  • Alessandra Avila Montini
    • 3
  1. 1.Federal Institute of Education, Science and Technology of São Paulo (IFSP)CampinasBrazil
  2. 2.Computer Science DivisionBrazilian Aeronautics Institute of Technology (ITA)São José dos CamposBrazil
  3. 3.Department of AdministrationUniversity of São Paulo (USP)São PauloBrazil

Personalised recommendations