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. Language: English