Skip to main content

Advertisement

Springer Nature Link
Log in

Application and comparison of several machine learning algorithms and their integration models in regression problems

  • ATCI 2019
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

With the rapid development of machine learning technology, as a regression problem that helps people to find the law from the massive data to achieve the prediction effect, more and more people pay attention. Data prediction has become an important part of people’s daily life. Currently, the technology is widely used in many fields such as weather forecasting, medical diagnosis and financial forecasting. Therefore, the research of machine learning algorithms in regression problems is a research hotspot in the field of machine learning in recent years. However, real-world regression problems often have very complex internal and external factors, and various machine learning algorithms have different effects on scalability and predictive performance. In order to better study the application effect of machine learning algorithm in regression problem, this paper mainly adopts three common machine learning algorithms: BP neural network, extreme learning machine and support vector machine. Then, by comparing the effects of the single model and integrated model of these machine learning algorithms in the application of regression problems, the advantages and disadvantages of each machine learning algorithm are studied. Finally, the performance of each machine learning algorithm in regression prediction is verified by simulation experiments on four different data sets. The results show that the research on several machine learning algorithms and their integration models has certain feasibility and rationality.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
€32.70 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (France)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Wu S, Wang L, Wu Z (2004) Prediction of port container throughput using improved grey model. Water Transp Manag 26(11):14–16

    Google Scholar 

  2. Wei F, Huang J (2010) Predictability of statistical downscaling of summer precipitation in eastern China. Acta Trop Meteorol 26(4):483–490

    Google Scholar 

  3. Zhang M, Zhang Y (2006) Container throughput forecast of Qingdao Port based on cubic exponential smoothing method. J Trop Meteorol Contain 07:37–39

    Google Scholar 

  4. Guo J, Huang W, Willians B (2014) Two adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification. Transp Res Part C Emerg Technol 43:50–64

    Article  Google Scholar 

  5. Xue JN, Shi ZK (2014) Short-time traffic flow prediction based on chaos time series theory. J Transp Syst Eng Inf Technol 8(5):68–72

    Google Scholar 

  6. Viana DR, Sansigolo CA (2016) Monthly and seasonal rainfall forecasting in Southern Brazil using multiple discriminant analysis. Weather Forecast 31(6):1947–1960

    Article  Google Scholar 

  7. Baser F, Demirhan F (2017) A fuzzy regression with support vector machine approach to the estimation of horizontal global solar radiation. Energy 123:229–240

    Article  Google Scholar 

  8. Jingjing H, Xiaolei H, Changyou Z (2016) Proactive service selection based on acquaintance model and LS-SVM. Neurocomputing 211:60–65

    Article  Google Scholar 

  9. Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29(5):1189–1232

    Article  MathSciNet  MATH  Google Scholar 

  10. Duan J, Ding C, Lu Y et al (2017) Short-term passenger flow forecasting method for rail transit sites considering dynamic volatility. J Traffic Inf Saf 35(05):68–75

    Google Scholar 

  11. Ma X, Ding C, Luan S et al (2017) Prioritizing influential factors for freeway incident clearance time prediction using the gradient boosting decision trees method. IEEE Trans Intel Transp Syst 99:1–8

    Google Scholar 

  12. Makridakis S, Winkler RL (1983) Averages of forecasts-some empirical results. Manag Sci 29(9):987–996

    Article  Google Scholar 

  13. Granger CWJ, Ramanathan R (1984) Improved methods of combining. J Forecast 3:197–204

    Article  Google Scholar 

  14. Diebold FX, Pauly P (1987) Structural change and the combination forecast. J Forecast 6:21–40

    Article  Google Scholar 

  15. Fu G, Tang X, Zeng Y (1995) Research on generalized recursive variance reciprocal combination forecasting method. J Univ Electron Sci Technol China 24(2):211–217

    Google Scholar 

  16. Chen H (2001) Research on combined prediction model based on prediction validity. Prediction 20(3):72–73

    Google Scholar 

  17. Chen H, Hou D (2003) Combined forecasting model based on standard deviation for predictive validity. J Syst Eng 15(3):203–210

    MathSciNet  Google Scholar 

  18. Jingrong D, Xiusi Y (1999) Research on nonlinear combination forecasting method based on fuzzy logic system. J Manag Sci 3:28–33

    Google Scholar 

  19. Dong J (2000) Research on nonlinear combination forecasting method based on wavelet network. J Syst Eng 4:383–388

    Google Scholar 

  20. Cao Y, Zhang D (2004) Nonlinear intelligent combination forecasting model and its application. J China Univ Min Technol 4:428–432

    Google Scholar 

  21. Liu C, Zhang Q (2007) Dynamic prediction of container throughput based on time series BP neural network. Water Transp Eng 1:4–7

    Google Scholar 

  22. Zhen Y, Hao M, Lu B et al (2015) Research on medium and long-term precipitation prediction model based on random forest. Hydropower Sci 6:6–10

    Google Scholar 

  23. Stoll S, Abbot-Smith K, Lieven E (2010) Lexically restricted utterances in Russian, German, and English child-directed speech. Cognit Sci 33(1):75–103

    Article  Google Scholar 

  24. Wu H, Liu G (2016) Forecast of Ningbo Port container sea-rail combined transportation based on grey RBF combined model. J Ningbo Univ (Nat Sci Ed) 29(4):123–127

    Google Scholar 

  25. Wen P, Wang T (2016) Research on container throughput prediction of Nantong port by combined model. China Bus Rev 8:138–141

    Google Scholar 

  26. Zhu Y (2016) Artificial intelligence revolution “combustion promoter”: machine learning. Sci Technol Rev 34(7):64–66

    Google Scholar 

  27. Ibrahim AO, Shamsuddin SM, Abraham A et al (2019) Adaptive memetic method of multi-objective genetic evolutionary algorithm for backpropagation neural network. Neural Comput Appl 4:1–18

    Google Scholar 

  28. Geng G, Luo X, Xiao Y (2005) Statistical learning theory and support vector machine. China Sci Technol Inf 12:178

    Google Scholar 

  29. Tan L, Liu H, Tan L (2014) Prediction and analysis of shanghai composite index based on extreme learning machine. J North China Univ Sci Technol 4:57–60

    Google Scholar 

  30. Xing X (2019) Ocean Mammalian sound recognition based on feature fusion. Rev Cient 29(3):653–664

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Yango University, Fuzhou, 350015, China

    Jui-Chan Huang & Kuo-Min Ko

  2. Department of Industrial Engineering and Management, National Kaohsiung University of Science and Technology, Kaohsiung City, 80778, Taiwan

    Ming-Hung Shu

  3. Department of Industrial Engineering and Management, Cheng Shiu University, Kaohsiung City, Taiwan

    Bi-Min Hsu

  4. Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung City, 80778, Taiwan

    Ming-Hung Shu

Authors
  1. Jui-Chan Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kuo-Min Ko
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ming-Hung Shu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bi-Min Hsu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kuo-Min Ko.

Ethics declarations

Conflict of interest

We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, and there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, JC., Ko, KM., Shu, MH. et al. Application and comparison of several machine learning algorithms and their integration models in regression problems. Neural Comput & Applic 32, 5461–5469 (2020). https://doi.org/10.1007/s00521-019-04644-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-019-04644-5

Keywords

Search

Navigation