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.

1 Less is More.- 2 Feature Weighting for Lazy Learning Algorithms.- 3 The Wrapper Approach.- 4 Data-driven Constructive Induction: Methodology and Applications.- 5 Selecting Features by Vertical Compactness of Data.- 6 Relevance Approach to Feature Subset Selection.- 7 Novel Methods for Feature Subset Selection with Respect to Problem Knowledge.- 8 Feature Subset Selection Using A Genetic Algorithm.- 9 A Relevancy Filter for Constructive Induction.- 10 Lexical Contextual Relations for the Unsupervised Discovery of Texts Features.- 11 Integrated Feature Extraction Using Adaptive Wavelets.- 12 Feature Extraction via Neural Networks.- 13 Using Lattice-based Framework as a Tool for Feature Extraction.- 14 Constructive Function Approximation.- 15 A Comparison of Constructing Different Types of New Feature for Decision Tree Learning.- 16 Constructive Induction: Covering Attribute Spectrum.- 17 Feature Construction Using Fragmentary Knowledge.- 18 Constructive Induction on Continuous Spaces.- 19 Evolutionary Feature Space Transformation.- 20 Feature Transformation by Function Decomposition.- 21 Constructive Induction of Cartesian Product Attributes.- 22 Towards Automatic Fractal Feature Extraction for Image Recognition.- 23 Feature Transformation Strategies for a Robot Learning Problem.- 24 Interactive Genetic Algorithm Based Feature Selection and Its Application to Marketing Data Analysis.