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Computational Intelligence for Missing Data Imputation, Estimation, and Management: Knowledge Optimization Techniques

Tshilidzi Marwala

ISBN13: 9781605663364|ISBN10: 1605663360|EISBN13: 9781605663371|ISBN13 Softcover: 9781616925604

DOI: 10.4018/978-1-60566-336-4

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Marwala, Tshilidzi. Computational Intelligence for Missing Data Imputation, Estimation, and Management: Knowledge Optimization Techniques. IGI Global, 2009. https://doi.org/10.4018/978-1-60566-336-4

APA

Marwala, T. (2009). Computational Intelligence for Missing Data Imputation, Estimation, and Management: Knowledge Optimization Techniques. IGI Global. https://doi.org/10.4018/978-1-60566-336-4

Chicago

Marwala, Tshilidzi. Computational Intelligence for Missing Data Imputation, Estimation, and Management: Knowledge Optimization Techniques. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-336-4

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The issue of missing data imputation has been extensively explored in information engineering, though needing a new focus and approach in research.

Computational Intelligence for Missing Data Imputation, Estimation, and Management: Knowledge Optimization Techniques focuses on methods to estimate missing values given to observed data. Providing a defining body of research valuable to those involved in the field of study, this book presents current and new computational intelligence techniques that allow computers to learn the underlying structure of data.

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Front Materials

Title Page

Nomenclature

Foreword

Preface

Acknowledgment

About the Author

Chapters

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Chapter 1

Introduction to Missing Data (pages 1-18)

Tshilidzi Marwala

In this chapter, the traditional missing data imputation issues such as missing data patterns and mechanisms are described. Attention is paid to the best models to deal with particular missing data mechanisms. A review of traditional...

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Chapter 2

Estimation of Missing Data Using Neural Networks and Genetic Algorithms (pages 19-44)

Tshilidzi Marwala

Missing data creates various problems in analyzing and processing data in databases. In this chapter, a method aimed at approximating missing data in a database that uses a combination of genetic algorithms and neural networks is...

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Chapter 3

A Hybrid Approach to Missing Data: Bayesian Neural Networks, Principal Component Analysis and Genetic Algorithms (pages 45-70)

Tshilidzi Marwala

The problem of missing data in databases has recently been dealt with through the use computational intelligence. The hybrid of auto-associative neural networks and genetic algorithms has proven to be a successful approach to missing...

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Chapter 4

Maximum Expectation Algorithms for Missing Data Estimation (pages 71-93)

Tshilidzi Marwala

Two sets of hybrid techniques have recently emerged for the imputation of missing data. These are, first, the combination of the Gaussian Mixtures Model and the Expectation Maximization algorithms (the GMM-EM) and second, the...

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Chapter 5

Missing Data Estimation Using Rough Sets (pages 94-116)

Tshilidzi Marwala

A number of techniques for handling missing data have been presented and implemented. Most of these proposed techniques are unnecessarily complex and, therefore, difficult to use. This chapter investigates a hot-deck data imputation...

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Chapter 6

Support Vector Regression for Missing Data Estimation (pages 117-141)

Tshilidzi Marwala

This chapter develops and compares the merits of three different data imputation models by using accuracy measures. The three methods are auto-associative neural networks, a principal component analysis and support vector regression...

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Chapter 7

Committee of Networks for Estimating Missing Data (pages 142-164)

Tshilidzi Marwala

This chapter introduces a committee of networks for estimating missing data. The first committee of networks consists of multi-layer perceptrons (MLPs), support vector machines (SVMs) and radial basis functions (RBFs). The committee...

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Chapter 8

Online Approaches to Missing Data Estimation (pages 165-186)

Tshilidzi Marwala

The use of inferential sensors is a common task for online fault detection in various control applications. A problem arises when sensors fail when the system is designed to make a decision based on the data from those sensors....

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Chapter 9

Missing Data Approaches to Classification (pages 187-209)

Tshilidzi Marwala

In this chapter, a classifier technique that is based on a missing data estimation framework that uses autoassociative multi-layer perceptron neural networks and genetic algorithms is proposed. The proposed method is tested on a set...

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Chapter 10

Optimization Methods for Estimation of Missing Data (pages 210-232)

Tshilidzi Marwala

This chapter presents various optimization methods to optimize the missing data error equation, which is made out of the autoassociative neural networks with missing values as design variables. The four optimization techniques that...

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Chapter 11

Estimation of Missing Data Using Neural Networks and Decision Trees (pages 233-255)

Tshilidzi Marwala

This chapter introduces a novel paradigm to impute missing data that combines a decision tree, autoassociative neural network (AANN) model and a principal component analysis-neural network (PCA-NN) based model. These models are...

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Chapter 12

Control of Biomedical System Using Missing Data Approaches (pages 256-275)

Tshilidzi Marwala

Neural networks are used in this chapter for classifying the HIV status of individuals based on socioeconomic and demographic characteristics. The trained network is then used to create an error equation with one of the demographic...

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Chapter 13

Emerging Missing Data Estimation Problems: Heteroskedasticity; Dynamic Programming and Impact of Missing Data (pages 276-301)

Tshilidzi Marwala

This chapter is divided into three parts: The first part presents a computational intelligence approach for predicting missing data in the presence of concept drift using an ensemble of multi-layered feed-forward neural networks. An...

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Computational Intelligence for Missing Data Imputation, Estimation, and Management: Knowledge Optimization Techniques

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