A Stratified Sampling Algorithm for Artificial Neural Networks
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.
KeywordsArtificial 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.S. Haykin, Neural Networks and Learning Machines (Pearson Education, Inc., Upper Saddle River, NJ, 2009)Google Scholar
- 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.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.S. Russell, P. Norvig, Artificial Intelligence. A Modern Approach (Prentice Hall, 2003)Google Scholar
- 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.The MathWorks, Inc, MATLAB Software [Online], https://www. mathworks.com/products/matlab.html. Accessed 06 Apr 2017
- 7.W.J. Stevenson, B.J. Isselhardt, Study Guide to Accompany Business Statistics: Concepts and Applications, 2nd edn (Harper & Row, 1978)Google Scholar
- 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.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