New method of remedying missing values based on support vector regression model

Sen Lin Luo*, Bin Liu, Li Min Pan, Ming De Ye, Zhao Yuan Ma, Tie Mei Zhang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In the actual data there are a lot of missing values which can be properly remedied by other relevant factors in order to bring down the amount of missing information. In this paper Support Vector Regression (SVR) is applied to predict the values of abdominal circumference, body mass index and high density lipoproteins. After predicted by SVR, the average relative errors of abdominal circumference, body mass index and high density lipoproteins are respectively 4.39%, 5.73% and 11.08%, the mean absolute errors of abdominal circumference, body mass index and high density lipoproteins are respectively 3.55, 1.41 and 0.14, and the RMS errors of abdominal circumference, body mass index and high density lipoproteins are respectively 4.54, 1.8 and 0.18. Compared with other methods, the experimental results show that the mean prediction error of SVR is the smallest.

Original languageEnglish
Title of host publication2010 International Conference on Biomedical Engineering and Computer Science, ICBECS 2010
DOIs
Publication statusPublished - 2010
Event2010 International Conference on Biomedical Engineering and Computer Science, ICBECS 2010 - Wuhan, China
Duration: 23 Apr 201025 Apr 2010

Publication series

Name2010 International Conference on Biomedical Engineering and Computer Science, ICBECS 2010

Conference

Conference2010 International Conference on Biomedical Engineering and Computer Science, ICBECS 2010
Country/TerritoryChina
CityWuhan
Period23/04/1025/04/10

Keywords

  • Missing values
  • Regression model
  • Support vector regression

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