Abstract

Improving the division accuracy and efficiency of continuous variables has always been an important direction of decision tree research. This article briefly introduces the development of decision tree, focuses on the two types of decision tree algorithms for non-traditional continuous variables — based on CART and based on statistical models. Finally, the future development trend of decision tree algorithms for continuous variables is discussed.

Details

Title
A Review of Decision Tree Classification Algorithms for Continuous Variables
Author
Jiao, S R 1 ; Song, J 1 ; Liu, B 1 

 Capital University of Economics and Business, school of statistics, Beijing, 100070, China 
Publication year
2020
Publication date
Nov 2020
Publisher
IOP Publishing
ISSN
17426588
e-ISSN
17426596
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2620854246
Copyright
© 2020. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.