Ebook: Feature Extraction, Construction and Selection: A Data Mining Perspective

There is broad interest in feature extraction, construction, and selection among practitioners from statistics, pattern recognition, and data mining to machine learning. Data preprocessing is an essential step in the knowledge discovery process for real-world applications. This book compiles contributions from many leading and active researchers in this growing field and paints a picture of the state-of-art techniques that can boost the capabilities of many existing data mining tools. The objective of this collection is to increase the awareness of the data mining community about the research of feature extraction, construction and selection, which are currently conducted mainly in isolation. This book is part of our endeavor to produce a contemporary overview of modern solutions, to create synergy among these seemingly different branches, and to pave the way for developing meta-systems and novel approaches. Even with today's advanced computer technologies, discovering knowledge from data can still be fiendishly hard due to the characteristics of the computer generated data. Feature extraction, construction and selection are a set of techniques that transform and simplify data so as to make data mining tasks easier. Feature construction and selection can be viewed as two sides of the representation problem.

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There is a broad interest in feature extraction, construction, and selection among practitioners from statistics, pattern recognition, and data mining to machine learning. Data pre-processing is an essential step in the knowledge discovery process for real-world applications. This book compiles contributions from many leading and active researchers in this growing field and paints a picture of the state-of-the-art techniques that can boost the capabilities of many existing data mining tools.
The objective of this collection is to increase the awareness of the data mining community about research into feature extraction, construction and selection, which are currently conducted mainly in isolation. This book is part of an endeavor to produce a contemporary overview of modern solutions, to create synergy among these seemingly different branches, and to pave the way for developing meta-systems and novel approaches.
The book can be used by researchers and graduate students in machine learning, data mining, and knowledge discovery, who wish to understand techniques of feature extraction, construction and selection for data pre-processing and to solve large size, real-world problems. The book can also serve as a reference work for those who are conducting research into feature extraction, construction and selection, and are ready to meet the exciting challenges ahead of us.

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There is a broad interest in feature extraction, construction, and selection among practitioners from statistics, pattern recognition, and data mining to machine learning. Data pre-processing is an essential step in the knowledge discovery process for real-world applications. This book compiles contributions from many leading and active researchers in this growing field and paints a picture of the state-of-the-art techniques that can boost the capabilities of many existing data mining tools.
The objective of this collection is to increase the awareness of the data mining community about research into feature extraction, construction and selection, which are currently conducted mainly in isolation. This book is part of an endeavor to produce a contemporary overview of modern solutions, to create synergy among these seemingly different branches, and to pave the way for developing meta-systems and novel approaches.
The book can be used by researchers and graduate students in machine learning, data mining, and knowledge discovery, who wish to understand techniques of feature extraction, construction and selection for data pre-processing and to solve large size, real-world problems. The book can also serve as a reference work for those who are conducting research into feature extraction, construction and selection, and are ready to meet the exciting challenges ahead of us.
Content:
Front Matter....Pages i-xxiv
Front Matter....Pages 1-1
Less Is More....Pages 3-12
Feature Weighting for Lazy Learning Algorithms....Pages 13-32
The Wrapper Approach....Pages 33-50
Data-Driven Constructive Induction: Methodology and Applications....Pages 51-68
Front Matter....Pages 69-69
Selecting Features by Vertical Compactness of Data....Pages 71-84
Relevance Approach to Feature Subset Selection....Pages 85-99
Novel Methods for Feature Subset Selection with Respect to Problem Knowledge....Pages 101-116
Feature Subset Selection Using a Genetic Algorithm....Pages 117-136
A Relevancy Filter for Constructive Induction....Pages 137-154
Front Matter....Pages 155-155
Lexical Contextual Relations for the Unsupervised Discovery of Texts Features....Pages 157-173
Integrated Feature Extraction Using Adaptive Wavelets....Pages 175-189
Feature extraction via Neural networks....Pages 191-204
Using Lattice-Based Framework as a Tool for Feature Extraction....Pages 205-218
Constructive Function Approximation....Pages 219-235
Front Matter....Pages 237-237
Constructive Induction: Covering Attribute Spectrum....Pages 239-255
Feature Construction Using Fragmentary Knowledge....Pages 257-272
Constructive Induction on Continuous Spaces....Pages 273-288
Front Matter....Pages 289-303
Evolutionary Feature Space Transformation....Pages 305-305
Feature Transformation By Function Decomposition....Pages 307-323
Front Matter....Pages 325-340
Constructive Induction of Cartesian Product Attributes....Pages 305-305
Front Matter....Pages 341-354
Towards Automatic Fractal Feature Extraction For Image Recognition....Pages 355-355
Feature Transformation Strategies for a Robot Learning Problem....Pages 357-373
Interactive Genetic Algorithm Based Feature Selection and Its Application to Marketing Data Analysis....Pages 375-391
Back Matter....Pages 393-406
....Pages 407-410