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Improving the Training Pattern in Back-Propagation Neural Networks Using Holt-Winters’ Seasonal Method and Gradient Boosting Model

  • S. Brilly SangeethaEmail author
  • N. R. Wilfred Blessing
  • N. Yuvaraj
  • J. Adeline Sneha
Chapter
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Part of the Algorithms for Intelligent Systems book series (AIS)

Abstract

In this paper, we propose an improved training pattern in back-propagation neural networks using Holt-Winters’ seasonal method and gradient boosting model (NHGB). It removes the errors that cause disabilities in the hidden layers of BPNN and further improves the predictive performance. It increases the weights and decays the error using Holt-Winters’ seasonal method and gradient boosting model, which reduces longer convergence time. The NHGB method is compared with other existing methods against average initial error, root mean square error, accuracy, sensitivity, and specificity metrics. The result shows that NHGB method is effective in terms of reduced RMSE and increased accuracy in classifying the datasets.

Keywords

Back-propagation neural network Artificial neural network Weight adjustment Holt-Winters’ seasonal method and gradient boosting model 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • S. Brilly Sangeetha
    • 1
    Email author
  • N. R. Wilfred Blessing
    • 2
  • N. Yuvaraj
    • 3
  • J. Adeline Sneha
    • 4
  1. 1.Department of Computer Science and EngineeringIES College of EngineeringThrissurIndia
  2. 2.Department of Information TechnologySalalah College of TechnologySalalahSultanate of Oman
  3. 3.Department of Computer Science and EngineeringSt. Peter’s Institute of Higher Education and ResearchChennaiIndia
  4. 4.Satyabhama Institute of Science and TechnologyChennaiIndia

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