Abstract
Support vector regression has been widely used in engineering data modeling. The regression relationship of engineering data usually varies greatly, which results that it being difficult to evaluate through a unified prediction model. In this work, a decision tree-assisted support vector regression is proposed, which can take advantage of training samples partition to improve prediction accuracy. A decision tree is proposed to partition the training samples in such a way that the samples in the same part have a more similar regression relationship to each other than to those in the other parts. Support vector regression is used to evaluate the regression relationship, and an optimization algorithm is designed to search the best splitting input variable and division point at each node. For a new sample to predict, it is compared from the root node of the decision tree until reaches a certain leaf node, and the response is obtained according to the prediction model contained in the leaf node. Experiments show that the proposed method is able to provide competitive prediction results compared with the conventional prediction methods.
Similar content being viewed by others
References
B. Agard and A. Kusiak, Data-mining-based methodology for the design of product families, International Journal of Production Research, 42(15) (2004) 2955–2969.
O. Y. Al-Jarrah, P. D. Yoo, S. Muhaidat, G. K. Karagiannidis and K. Taha, Efficient machine learning for big data: a review, Big Data Research, 2(3) (2015) 87–93.
S. Shamshirband, D. Petković, A. Amini, N. B. Anuar, V. Nikolić, Ž. Ćojbašić, M. Kiah and A. Gani, Support vector regression methodology for wind turbine reaction torque prediction with power-split hydrostatic continuous variable transmission, Energy, 67 (2014) 623–630.
T. Benkedjouh, K. Medjaher, N. Zerhouni and S. Rechak, Health assessment and life prediction of cutting tools based on support vector regression, Journal of Intelligent Manufacturing, 26(2) (2015) 213–223.
Z. Sun, L. Wang, J. Q. Zhou and C. Wang, A new method for determining the hydraulic aperture of rough rock fractures using the support vector regression, Engineering Geology, 271 (2020) 105618.
S. K. Singh and A. K. Gupta, Application of support vector regression in predicting thickness strains in hydro-mechanical deep drawing and comparison with ANN and FEM, CIRP Journal of Manufacturing Science and Technology, 3(1) (2010) 66–72.
Z. Wei, T. Tao, D. ZhuoShu and E. Zio, A dynamic particle filter-support vector regression method for reliability prediction, Reliability Engineering and System Safety, 119 (2013) 109–116.
J. Hamidzadeh, IRDDS: instance reduction based on distance-based decision surface, Journal of AI and Data Mining, 3(2) (2015) 121–130.
M. Shi, L. Zhang, W. Sun and X. Song, A fuzzy c-means algorithm guided by attribute correlations and its application in the big data analysis of tunnel boring machine, Knowledge-Based Systems, 182 (2019) 104859.
V. Muralidharan and V. Sugumaran, Feature extraction using wavelets and classification through decision tree algorithm for fault diagnosis of mono-block centrifugal pump, Measurement, 46(1) (2013) 353–359.
M. Amarnath, D. Jain, V. Sugumaran and H. Kumar, Fault diagnosis of helical gearbox using decision tree and best-first tree, International Journal of Research in Mechanical Engineering, 1(1) (2013) 22–33.
E. Ghasemi, H. Gholizadeh and A. C. Adoko, Evaluation of rockburst occurrence and intensity in underground structures using decision tree approach, Engineering with Computers, 36(1) (2020) 213–225.
A. Sumesh, B. B. Nair, K. Rameshkumar, A. Santhakumari, A. Raja and K. Mohandas, Decision tree based weld defect classification using current and voltage signatures in GMAW process, Materials Today: Proceedings, 5(2) (2018) 8354–8363.
R. Mukherjee and A. De, Real-time dynamic security analysis of power systems using strategic PMU measurements and decision tree classification, Electrical Engineering, 103(2) (2021) 813–824.
A. J. Smola and B. Schölkopf, A tutorial on support vector regression, Statistics and Computing, 14(3) (2004) 199–222.
S. Mirjalili, S. M. Mirjalili and A. Lewis, Grey wolf optimizer, Advances in Engineering Software, 69 (2014) 46–61.
Surrogates Toolbox User’s Guide, https://sites.google.com/site/srgtstoolbox/, Version 3.0, FAC Viana (2011).
P. Jiang, Q. Zhou and X. Shao, Surrogate Model-based Engineering Design and Optimization, Springer, Berlin (2020).
D. Dua and C. Graff, UCI Machine Learning Repository, http://archive.ics.uci.edu/ml, School of Information and Computer Science, University of California, Irvine, CA, USA (2019).
S. J. Sheather and M. C. Jones, A reliable data-based bandwidth selection method for kernel density estimation, Journal of the Royal Statistical Society: Series B (Methodological), 53 (1991) 683–690.
I. Ortigosa, R. Lopez and J. Garcia, A neural networks approach to residuary resistance of sailing yachts prediction, Proceedings of the International Conference on Marine Engineering (2007) 250.
T. W. Simpson, T. M. Mauery, J. J. Korte and F. Mistree, Kriging models for global approximation in simulation-based multidisciplinary design optimization, AIAA Journal, 39(12) (2001) 2233–2241.
E. N. Ben-Ari and D. M. Steinberg, Modeling data from computer experiments: an empirical comparison of kriging with MARS and projection pursuit regression, Quality Engineering, 19(4) (2007) 327–338.
Acknowledgments
This work is supported by Natural Science Foundation of Jiangsu Province (BK20210777) and Funding of Jiangsu University (20JDG068).
Author information
Authors and Affiliations
Corresponding author
Additional information
Maolin Shi is a Research Associate of the School of Agricultural Engineering, Jiangsu University, Zhenjiang, China. He received his Ph.D. in Mechanical Design and Theory from Dalian University of Technology, Dalian, China. His research interests include engineering data-driven techniques, surrogate model, and data clustering.
Rights and permissions
About this article
Cite this article
Shi, M. SVRT: a decision tree-assisted support vector regression for modeling engineering data with complex regression relationship. J Mech Sci Technol 36, 2471–2480 (2022). https://doi.org/10.1007/s12206-022-0429-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12206-022-0429-7