Estimation of Missing Data Using Neural Networks and Decision Trees

Estimation of Missing Data Using Neural Networks and Decision Trees

Tshilidzi Marwala (University of Witwatersrand, South Africa)
DOI: 10.4018/978-1-60566-336-4.ch011
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Abstract

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 designed to answer the crucial question of whether the optimization bounds actually matter. For each model, the decision tree is used to predict search bounds for a hybrid simulated annealing and genetic algorithm method that minimizes an error function derived from the respective model. The models’ ability to impute missing data is tested and then compared using HIV sero-prevalance data. Results indicate an average increase in accuracy of 13% with the AANN based model’s average accuracy increasing from 75.8% to 86.3% while that of the PCA-NN based model increasing from 66.1% to 81.6%.

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Table of Contents
Foreword
Fulufhelo Vincent Nelwamondo
Preface
Tshilidzi Marwala
Acknowledgment
Tshilidzi Marwala
About the Author
Chapter 1
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... Sample PDF
Introduction to Missing Data
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Chapter 2
Tshilidzi Marwala
Missing data creates various problems in analyzing and processing data in databases. In this chapter, a method aimed at approximating missing data... Sample PDF
Estimation of Missing Data Using Neural Networks and Genetic Algorithms
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Chapter 3
Tshilidzi Marwala
The problem of missing data in databases has recently been dealt with through the use computational intelligence. The hybrid of auto-associative... Sample PDF
A Hybrid Approach to Missing Data: Bayesian Neural Networks, Principal Component Analysis and Genetic Algorithms
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Chapter 4
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... Sample PDF
Maximum Expectation Algorithms for Missing Data Estimation
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Chapter 5
Tshilidzi Marwala
A number of techniques for handling missing data have been presented and implemented. Most of these proposed techniques are unnecessarily complex... Sample PDF
Missing Data Estimation Using Rough Sets
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Chapter 6
Tshilidzi Marwala
This chapter develops and compares the merits of three different data imputation models by using accuracy measures. The three methods are... Sample PDF
Support Vector Regression for Missing Data Estimation
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Chapter 7
Tshilidzi Marwala
This chapter introduces a committee of networks for estimating missing data. The first committee of networks consists of multi-layer perceptrons... Sample PDF
Committee of Networks for Estimating Missing Data
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Chapter 8
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... Sample PDF
Online Approaches to Missing Data Estimation
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Chapter 9
Tshilidzi Marwala
In this chapter, a classifier technique that is based on a missing data estimation framework that uses autoassociative multi-layer perceptron neural... Sample PDF
Missing Data Approaches to Classification
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Chapter 10
Tshilidzi Marwala
This chapter presents various optimization methods to optimize the missing data error equation, which is made out of the autoassociative neural... Sample PDF
Optimization Methods for Estimation of Missing Data
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Chapter 11
Tshilidzi Marwala
This chapter introduces a novel paradigm to impute missing data that combines a decision tree, autoassociative neural network (AANN) model and a... Sample PDF
Estimation of Missing Data Using Neural Networks and Decision Trees
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Chapter 12
Tshilidzi Marwala
Neural networks are used in this chapter for classifying the HIV status of individuals based on socioeconomic and demographic characteristics. The... Sample PDF
Control of Biomedical System Using Missing Data Approaches
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Chapter 13
Tshilidzi Marwala
This chapter is divided into three parts: The first part presents a computational intelligence approach for predicting missing data in the presence... Sample PDF
Emerging Missing Data Estimation Problems: Heteroskedasticity; Dynamic Programming and Impact of Missing Data
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