Intelligent Data Analysis: Developing New

Intelligent Data Analysis: Developing New Methodologies Through Pattern Discovery and Recovery covers a variety of issues in relation to intelligent data analysis and brings together current research, results, problems, and applications from both theoretical and practical approaches.

Introduction
Automatic Intelligent Data Analysis
Random Fuzzy Sets
Pattern Discovery in Gene Expression Data
Using "Blackbox" Algorithms Such AS TreeNET and Random Forests for Data-Ming and For Finding Meaningful Patterns, Relationships and Outliers in Complex Ecological Data: An Overview, An Example Using Golden Eagle Satellite Data and An Outlook For A Promising Future
A New Approach to Classification of Imbalanced Classes via Atanassov's Intuitionistic Fuzzy Sets
Pattern Discovery from Huge Data Set: Methodologies
Fuzzy Neural Network Models for Knowledge Discovery
Genetic Learning: Initialization and Representation Issues
Evolutionary Computing
Particle Identification Using Light Scattering: A Global Optimization Problem
Exact Markov Chain Monte Carlo Algorithms and Their Applications in Probabilistic Data Analysis and Inference
Pattern Discovery from Huge Data Set Applications
Design of Knowledge Bases for Forward and Reverse Mappings of TIG Welding Process
A Fuzzy Decision Tree Analysis of Traffic Fatalities in the US
New Churn Prediction Strategies in Telecom Industry
Intelligent Classification and Ranking Analyses Using CARBS: Bank Rating Applications
Analysis of Individual Risk Attitude for Risk Management Based on Cumulative Prospect Theory
Pattern Recovery from Small Data Set Methodologies and Applications
Neural Networks and Bootstrap Methods for Regression Models with Dependent Errors
Financial Crisis Modeling and Prediction with a Hilbert-EMD-based SVM Approach
Virtual Sampling with Data Construction Method
Compilation of References
Index
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Intelligent Data Analysis: Developing New Methodologies Through Pattern Discovery and Recovery

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Table of Contents

Supplemental Materials