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Semi-parametric optimization for missing data imputation

delete2007-01-18
delete88
PRE
AI
秦
秦永松 (Yongsong Qin)
S
Shichao Zhang *
X
Xiaofeng Zhu
J
Jilian Zhang
张
张承启 (Chengqi Zhang)
DOI：10.1007/s10489-006-0032-0delete
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Abstract

Abstract

En 中文
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.
Keywords:
missing data
missing data imputation
semi-parametric data

Journal

Applied Intelligence cover
Applied Intelligence
IF:
3.5
Papers:
7.5K
Citations:
1.7W

Organization

No organization information available