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

Description

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.


Table of Contents:
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 Analysis and 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

Chapter XII: Control of Biomedical System Using Missing Data Approaches

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

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