Title
Application and comparison of several machine learning algorithms and their integration models in regression problems
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.
Year
DOI
Venue
2020
10.1007/s00521-019-04644-5
Neural Computing and Applications
Keywords
DocType
Volume
Machine learning, Regression problem, BP neural network, Extreme learning machine, Support vector machine
Journal
32
Issue
ISSN
Citations 
10
0941-0643
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Jui-Chan Huang100.68
Kuo-Min Ko200.34
Ming-Hung Shu321624.21
Bi-Min Hsu47810.18