A Novel Nonparametric Multiple Imputation Algorithm for Estimating Missing Data

被引：0
|
作者：
Gheyas, Iffat A. [1 ]
Smith, Leslie S. [1 ]
机构：
[1] Univ Stirling, Dept Comp Sci & Math, Stirling FK9 4LA, Scotland
来源：
WORLD CONGRESS ON ENGINEERING 2009, VOLS I AND II | 2009年
关键词：
Missing values; imputation; single imputation; multiple imputation;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
The treatment of incomplete data is an important step in pre-processing data prior to later analysis. We propose a novel non-parametric multiple imputation algorithm for estimating missing value. The proposed algorithm is based on Generalized Regression Neural Networks. We compare the proposed algorithm against existing algorithms on forty-five real and synthetic datasets. The effectiveness of imputation algorithms is evaluated in classification problems. The performance of proposed algorithm appears to be superior to that of other algorithms.
引用
收藏
页码：1281 / 1286
页数：6
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