Table of Contents

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.

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