Missing data imputation in multivariate data by evolutionary algorithms

被引：19

作者：

Figueroa Garcia, Juan C. ^{[1
]}

Kalenatic, Dusko ^{[2
]}

Lopez Bello, Cesar Amilcar ^{[1
]}

机构：

[1] Univ Dist Francisco Jose de Caldas, Bogota, Colombia

[2] Univ Sabana, Chia, Colombia

来源：

COMPUTERS IN HUMAN BEHAVIOR | 2011年 / 27卷 / 05期

关键词：

Missing data; Evolutionary optimization; Multivariate analysis; Multiple data imputation;

D O I：

10.1016/j.chb.2010.06.026

中图分类号：

B84 [心理学];

学科分类号：

04 ; 0402 ;

摘要：

This paper presents a proposal based on an evolutionary algorithm to impute missing observations in multivariate data. A genetic algorithm based on the minimization of an error function derived from their covariance matrix and vector of means is presented. All methodological aspects of the genetic structure are presented. An extended explanation of the design of the fitness function is provided. An application example is solved by the proposed method. (C) 2010 Elsevier Ltd. All rights reserved.

引用

页码：1468 / 1474

页数：7

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