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Computational intelligence for missing data imputation, estimation and management: knowledge optimization techniques

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In recent years, the issue of missing data imputation has been extensively explored in information engineering.

Computational Intelligence for Missing Data Imputation, Estimation, and Management: Knowledge Optimization Techniques presents methods and technologies in estimation of missing values given the observed data. Providing a defining body of research valuable to those involved in the field of study, this book covers techniques such as radial basis functions, support vector machines, and principal component analysis.

Author(s): Tshilidzi Marwala
Series: Premier Reference Source
Publisher: Information Science Reference
Year: 2009

Language: English
Pages: 327
City: Hershey PA

Title......Page 2
Table of Contents......Page 4
Nomenclature......Page 9
Foreword......Page 11
Preface......Page 13
Acknowledgment......Page 20
Introduction to Missing Data......Page 22
Estimation of Missing DataUsing Neural Networks andGenetic Algorithms......Page 40
A Hybrid Approach toMissing Data:Bayesian Neural Networks,Principal Component Analysisand Genetic Algorithms......Page 66
Maximum ExpectationAlgorithms for Missing DataEstimation......Page 92
Missing Data Estimation UsingRough Sets......Page 115
Support Vector Regression forMissing Data Estimation......Page 138
Committee of Networks forEstimating Missing Data......Page 163
Online Approaches to MissingData Estimation......Page 186
Missing Data Approaches toClassification......Page 208
Optimization Methods forEstimation of Missing Data......Page 231
Estimation of Missing DataUsing Neural Networks andDecision Trees......Page 254
Control of Biomedical SystemUsing Missing Data Approaches......Page 277
Emerging Missing DataEstimation Problems:Heteroskedasticity; Dynamic Programmingand Impact of Missing Data......Page 297
About the Author......Page 323
Index......Page 324