Journal
APPLIED INTELLIGENCE
Volume 27, Issue 1, Pages 79-88Publisher
SPRINGER
DOI: 10.1007/s10489-006-0032-0
Keywords
missing data; missing data imputation; semi-parametric data
Categories
Ask authors/readers for more resources
Missing data imputation is an important issue in machine learning and data mining. In this paper, we propose a new and efficient imputation method for a kind of missing data: semi-parametric data. Our imputation method aims at making an optimal evaluation about Root Mean Square Error (RMSE), distribution function and quantile after missing-data are imputed. We evaluate our approaches using both simulated data and real data experimentally, and demonstrate that our stochastic semi-parametric regression imputation is much better than existing deterministic semi-parametric regression imputation in efficiency and effectiveness.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available