Development of the classifier based on a multilayer perceptron using genetic algorithm and cart decision tree

Authors

DOI:

https://doi.org/10.15587/1729-4061.2021.242795

Keywords:

neural network, multilayer perceptron using a genetic algorithm, CART decision tree

Abstract

The problem of developing universal classifiers of biomedical data, in particular those that characterize the presence of a large number of parameters, inaccuracies and uncertainty, is urgent. Many studies are aimed at developing methods for analyzing these data, among them there are methods based on a neural network (NN) in the form of a multilayer perceptron (MP) using GA.

The question of the application of evolutionary algorithms (EA) for setting up and learning the neural network is considered.

Theories of neural networks, genetic algorithms (GA) and decision trees intersect and penetrate each other, new developed neural networks and their applications constantly appear.

An example of a problem that is solved using EA algorithms is considered. Its goal is to develop and research a classifier for the diagnosis of breast cancer, obtained by combining the capabilities of the multilayer perceptron using the genetic algorithm (GA) and the CART decision tree.

The possibility of improving the classifiers of biomedical data in the form of NN based on GA by applying the process of appropriate preparation of biomedical data using the CART decision tree has been established.

The obtained results of the study indicate that these classifiers show the highest efficiency on the set of learning and with the minimum reduction of Decision Trees; increasing the number of contractions usually degrades the simulation result. On two datasets on the test set, the simulation accuracy was »83–87 %.

The experiments carried out have confirmed the effectiveness of the proposed method for the synthesis of neural networks and make it possible to recommend it for practical use in processing data sets for further diagnostics, prediction, or pattern recognition

Author Biographies

Lyudmila Dobrovska, National Technical University of Ukraine «Igor Sikorsky Kyiv Polytechnic Institute»

PhD, Associate Professor

Department of Biomedical Cybernetics

Olena Nosovets, National Technical University of Ukraine «Igor Sikorsky Kyiv Polytechnic Institute»

PhD

Department of Biomedical Cybernetics

References

  1. Dobrovska, L., Dobrovska, I. (2015). Teoriia ta praktyka neironnykh merezh. Kyiv: NTUU «KPI», 395.
  2. Dobrovska, L., Dobrovska, I. (2017). Design of the universal classifier as a RBF network based on the CART solution tree. Eastern-European Journal of Enterprise Technologies, 4 (4 (88)), 63–71. doi: http://doi.org/10.15587/1729-4061.2017.108976
  3. Farizawani, A., Puteh, M., Marina, Y., Rivaie, A. (2020). A review of artificial neural network learning rule based on multiple variant of conjugate gradient approaches. Journal of Physics: Conference Series, 1529, 022040. doi: http://doi.org/10.1088/1742-6596/1529/2/022040
  4. Yao, X. (1993). A review of evolutionary artificial neural networks. International Journal of Intelligent Systems, 8 (4), 539–567. doi: http://doi.org/10.1002/int.4550080406
  5. Dutta, P., Kumar, A. (2018). Modeling and Optimization of a Liquid Flow Process using an Artificial Neural Network-Based Flower Pollination Algorithm. Journal of Intelligent Systems, 29 (1), 787–798. doi: http://doi.org/10.1515/jisys-2018-0206
  6. Venkatesan, D., Kannan, K., Saravanan, R. (2008). A genetic algorithm-based artificial neural network model for the optimization of machining processes. Neural Computing and Applications, 18 (2), 135–140. doi: http://doi.org/10.1007/s00521-007-0166-y
  7. Castillo, P., Arenas, M., Castillo-Valdivieso, J., Merelo, J., Prieto, A., Romero, G. (2003). Artificial Neural Networks Design using Evolutionary Algorithms. Advances In Soft Computing. London: Springer, 43–52. doi: http://doi.org/10.1007/978-1-4471-3744-3_5
  8. Ding, S., Xu, L., Su, C.,Zhu, H. (2010). Using Genetic Algorithms to Optimize Artificial Neural Networks. Journal Of Convergence Information Technology, 5 (8), 54–62. doi: http://doi.org/10.4156/jcit.vol5.issue8.6
  9. Jaafra, Y., Luc Laurent, J., Deruyver, A., Saber Naceur, M. (2019). Reinforcement learning for neural architecture search: A review. Image and Vision Computing, 89, 57–66. doi: http://doi.org/10.1016/j.imavis.2019.06.005
  10. Baker, B., Gupta, O., Naik, N., Raskar, R. (2021). Designing neural network architectures using reinforcement learning. Int. Conf. Learn. Represent. San Juan. Available at: https://arxiv.org/abs/1611.02167
  11. Miikkulainen, R., Liang, J., Meyerson, E., Rawal, A., Fink, D., Francon, O. et al. (2019). Evolving Deep Neural Networks. Artificial Intelligence In The Age Of Neural Networks And Brain Computing. Elsevier, 293–312. doi: http://doi.org/10.1016/b978-0-12-815480-9.00015-3
  12. Real, E., Aggarwal, A., Huang, Y., Le, Q. V. (2019). Regularized Evolution for Image Classifier Architecture Search. Proceedings of the AAAI Conference on Artificial Intelligence, 33, 4780–4789. doi: http://doi.org/10.1609/aaai.v33i01.33014780
  13. Matteucci, M. (2006). ELeaRNT: Evolutionary Learning of Rich Neural Network Topologies. Computer Science. doi: http://doi.org/10.21236/ada456062
  14. Luo, R., Tian, F., Qin, T., Chen, E., Liu, T. (2018). Neural Architecture Optimization. Conference And Workshop On Neural Information Processing Systems, 7827–7838.
  15. Sariev, E., Germano, G. (2019). Bayesian regularized artificial neural networks for the estimation of the probability of default. Quantitative Finance, 20 (2), 311–328. doi: http://doi.org/10.1080/14697688.2019.1633014
  16. Kim, H. B., Jung, S. H., Kim, T. G., Park, K. H. (1996). Fast learning method for back-propagation neural network by evolutionary adaptation of learning rates. Neurocomputing, 11 (1), 101–106. doi: http://doi.org/10.1016/0925-2312(96)00009-4
  17. Michel, D., Navarro, D. (2021). Genetic Operators and Their Impact on the Training of Deep Neural Networks. Metaheuristics In Machine Learning: Theory And Applications. Cham: Springer, 97–124. doi: http://doi.org/10.1007/978-3-030-70542-8_5
  18. UCI Machine Learning Repository: Data Sets. Available at: http://archive.ics.uci.edu/ml/datasets.php Last accessed: 22.09.2021

Downloads

Published

2021-10-31

How to Cite

Dobrovska, L., & Nosovets, O. (2021). Development of the classifier based on a multilayer perceptron using genetic algorithm and cart decision tree. Eastern-European Journal of Enterprise Technologies, 5(9 (113), 82–90. https://doi.org/10.15587/1729-4061.2021.242795

Issue

Section

Information and controlling system