Computational Intelligence for Missing Data Imputation, Estimation, and Management: Knowledge Optimization Techniques > Chapter XI: Estimation of Missing Data Using Neural Networks and Decision Trees > DISCUSSION - Pg. 248: Safari Books Online

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

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

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Contents

Table of Contents

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

Chapter I: Introduction to Missing Data

Chapter II: Estimation of Missing Data Using Neural Networks and Genetic Algorithms

Chapter III: A Hybrid Approach to Missing Data: Bayesian Neural Networks,Principal Component Analysisand Genetic Algorithms

Chapter IV: Maximum Expectation Algorithms for Missing Data Estimation

Chapter V: Missing Data Estimation Using Rough Sets

Chapter VI: Support Vector Regression for Missing Data Estimation

Chapter VII: Committee of Networks for Estimating Missing Data

Chapter VIII: Online Approaches to Missing Data Estimation

Chapter IX: Missing Data Approaches to Classification

Chapter X: Optimization Methods for Estimation of Missing Data

Chapter XI: Estimation of Missing Data Using Neural Networks and Decision Trees

THE MISSING DATA PROBLEM

DECISION-TREE, NEURAL NETWORK AND HYBRID SIMULATED ANNEALING AND GENETIC ALGORITHM APPROACH

HYBRID PRINCIPAL COMPONENT ANALYSIS, NEURAL NETWORK AND HYBRID SIMULATED ANNEALING GENETIC ALGORITHM (PCA-NN-HSAGA) APPROACH

EXPERIMENT AND RESULTS

Chapter XII: Control of Biomedical System Using Missing Data Approaches

Chapter XIII: Emerging Missing DataEstimation Problems: Heteroskedasticity; Dynamic Programming and Impact of Missing Data

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Marwala Figure.11.8..Optimization.nodes.for.the.PCA-NN-HSAGA.neural.network Figure.11.9..Optimization.results.for.the.PCA-NN-HSAGA.neural.network