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

  • Jui-Chan Huang
  • Kuo-Min KoEmail author
  • Ming-Hung Shu
  • Bi-Min Hsu
ATCI 2019
  • 7 Downloads

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.

Keywords

Machine learning Regression problem BP neural network Extreme learning machine Support vector machine 

Notes

Compliance with ethical standards

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.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Jui-Chan Huang
    • 1
  • Kuo-Min Ko
    • 1
    Email author
  • Ming-Hung Shu
    • 2
    • 4
  • Bi-Min Hsu
    • 3
  1. 1.Yango UniversityFuzhouChina
  2. 2.Department of Industrial Engineering and ManagementNational Kaohsiung University of Science and TechnologyKaohsiung CityTaiwan
  3. 3.Department of Industrial Engineering and ManagementCheng Shiu UniversityKaohsiung CityTaiwan
  4. 4.Department of Healthcare Administration and Medical InformaticsKaohsiung Medical UniversityKaohsiung CityTaiwan

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