TY - GEN
T1 - A regression forecasting model of carbon dioxide concentrations based-on principal component analysis-support vector machine
AU - Wang, Yiou
AU - Ding, Gangyi
AU - Liu, Laiyang
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2015.
PY - 2015
Y1 - 2015
N2 - We propose Principal Component Analysis-Support Vector Machine (PCA-SVM) to forecast the changes of regional carbon dioxide concentrations. Firstly, we get the most valuable principal components of the influencing factors (IF) of carbon dioxide concentrations by PCA. Then we use the output of PCA as the input of non-linear SVM to learn a regression forecasting model with radial basis function. Due to the introducing of PCA, we successfully eliminate the redundant and correlate information in IFs and reduce the computation cost of SVM. The results of the comparative experiment demonstrate that our PCA-SVM model is more effective and more efficient than the standard SVM. Moreover, we have tested different kernel functions in our PCA-SVM model, and the experimental results show that PCA-SVM model with radial basis function performs best respecting to the learning ability and generalization capability.
AB - We propose Principal Component Analysis-Support Vector Machine (PCA-SVM) to forecast the changes of regional carbon dioxide concentrations. Firstly, we get the most valuable principal components of the influencing factors (IF) of carbon dioxide concentrations by PCA. Then we use the output of PCA as the input of non-linear SVM to learn a regression forecasting model with radial basis function. Due to the introducing of PCA, we successfully eliminate the redundant and correlate information in IFs and reduce the computation cost of SVM. The results of the comparative experiment demonstrate that our PCA-SVM model is more effective and more efficient than the standard SVM. Moreover, we have tested different kernel functions in our PCA-SVM model, and the experimental results show that PCA-SVM model with radial basis function performs best respecting to the learning ability and generalization capability.
KW - Carbon dioxide concentrations
KW - Principal component analysis
KW - Regression and forecasting model
KW - Support vector machine
UR - http://www.scopus.com/inward/record.url?scp=84922352807&partnerID=8YFLogxK
U2 - 10.1007/978-3-662-45737-5_45
DO - 10.1007/978-3-662-45737-5_45
M3 - Conference contribution
AN - SCOPUS:84922352807
T3 - Communications in Computer and Information Science
SP - 447
EP - 457
BT - Geo-Informatics in Resource Management and Sustainable Ecosystem - 2nd International Conference, GRMSE 2014, Proceedings
A2 - Bian, Fuling
A2 - Xie, Yichun
PB - Springer Verlag
T2 - 2nd International Conference on Geo-Informatics in Resource Management and Sustainable Ecosystem, GRMSE 2014
Y2 - 3 October 2014 through 5 October 2014
ER -