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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hill T, Marquez L, O’Connor M, Remus W (1994) Artificial neural network models for forecasting and decision making. Int J Forecast 10(1):5–15
Wang L, Zeng Y, Chen T (2015) Back propagation neural network with adaptive differential evolution algorithm for time series forecasting. Expert Syst Appl 42(2):855–863
Zeng YR, Zeng Y, Choi B, Wang L (2017) Multifactor-influenced energy consumption forecasting using enhanced back-propagation neural network. Energy 127:381–396
Bai Y, Li Y, Wang X, Xie J, Li C (2016) Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions. Atmos Pollut Res 7(3):557–566
Mason C, Twomey J, Wright D, Whitman L (2018) Predicting engineering student attrition risk using a probabilistic neural network and comparing results with a back propagation neural network and logistic regression. Res Higher Educ 59(3):382–400
Sun W, Wang Y (2018) Short-term wind speed forecasting based on fast ensemble empirical mode decomposition, phase space reconstruction, sample entropy and improved back-propagation neural network. Energy Convers Manag 157:1–12
Ye Z, Kim MK (2018) Predicting electricity consumption in a building using an optimized back-propagation and Levenberg–Marquardt back-propagation neural network: Case study of a shopping mall in China. Sustain Cities Soc 42:176–183; Fröhlinghaus T, Weichert A, Rujan P (1994) Hierarchical neural networks for time-series analysis and control. Netw Comput Neural Syst 5(1):101–116
Chen T, Guestrin C (2016) Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 785–794
Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat: 1189–1232
Friedman J, Hastie T, Tibshirani R (2000) Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). Ann Stat 28(2):337–407
Murphy PM (1992) UCI Repository of machine learning databases [Machine-readable data repository]. In: Technical report. Department of Information and Computer Science, University of California
Zhao X, Han M, Ding L, Calin AC (2018) Forecasting carbon dioxide emissions based on a hybrid of mixed data sampling regression model and back propagation neural network in the USA. Environ Sci Pollut Res 25(3):2899–2910
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Brilly Sangeetha, S., Wilfred Blessing, N.R., Yuvaraj, N., Adeline Sneha, J. (2020). Improving the Training Pattern in Back-Propagation Neural Networks Using Holt-Winters’ Seasonal Method and Gradient Boosting Model. In: Johri, P., Verma, J., Paul, S. (eds) Applications of Machine Learning. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-3357-0_13
Download citation
DOI: https://doi.org/10.1007/978-981-15-3357-0_13
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-3356-3
Online ISBN: 978-981-15-3357-0
eBook Packages: EngineeringEngineering (R0)