E3S Web Conf.
Volume 214, 20202020 International Conference on Energy Big Data and Low-carbon Development Management (EBLDM 2020)
|Number of page(s)||7|
|Section||Machine Learning and Energy Industry Structure Forecast Analysis|
|Published online||07 December 2020|
Research on the order parameter selection algorithm based on correlation analysis and principal component analysis——Taking the Logistics sector in Gansu Province as an example
1 Gansu Business Development Research Center, Lanzhou University of Finance and Economics, Lanzhou, Chjna
2 Gansu Key Laboratory of E-Business Technology and Application, Lanzhou University of Finance and Economics, Lanzhou, Chjna
An order parameter selection algorithm based on correlation analysis and principal component analysis was designed according to the statistical analysis method, the selection principle of order parameters of social system, and the correlation test in correlation analysis and the variable contribution test in principal component analysis in this paper. The redundant variables were eliminated from the system by correlation analysis first, and then the variables with high contribution to the system were selected by principal component analysis, so the order parameters obtained accordingly not only have low information redundancy, but also reflect the actual information of the social system to the greatest extent. At the end of this paper, the logistics sector in Gansu Province was taken as an example to select the panel data from 2006 to 2015. Eight indices were extracted as the order parameters of the logistics sector in Gansu Province from the sixteen indices which are redundant selected by this algorithm. The order parameters selected by rational judgment reflect 99% of the original information. The results show that the order parameters in the social system can be correctly and reasonably selected by this order parameter selection algorithm based on correlation analysis and principal component analysis.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.