Title :
Based on support vector machine approach to missing data
Author :
Li-hua, Yang ; Qing-hua, Nie
Author_Institution :
Sch. of Inf. Eng., JingDeZhen Ceramic Inst., Jingdezhen, China
Abstract :
This paper systematically analyzes the causes and the mechanism of missing data, and research the processing method of missing data based on the support vector machine. And the results show that the prediction based on support vector machine method is more desirable than neural network, wavelet network model. And this method can promote and apply in the prediction of missing data to a certain extend.
Keywords :
data analysis; support vector machines; missing data; neural network; support vector machine; wavelet network model; Buildings; Fitting; Support vector machines; SVM; completing; missing values;
Conference_Titel :
System Science, Engineering Design and Manufacturing Informatization (ICSEM), 2011 International Conference on
Conference_Location :
Guiyang
Print_ISBN :
978-1-4577-0247-1
DOI :
10.1109/ICSSEM.2011.6081198