Advances in Feature Selection for Data and Pattern Recognition

Discusses recent developments and research trends in the field of feature selection and data and pattern recognition

Presents theoretical approaches as well as applications

Introduces novel propositions, highlighting and discussing properties of objects, analyzing intricacies of processes, and bounds on computational complexity

Inhalt
An Introduction.- Attribute Selection Based on Reduction of Numerical Attribute During Discretization.- Improving Bagging Ensembles for Class Imbalanced Data by Active Learning.- Optimization of Decision Rules Relative to Length Based on Modied Dynamic Programming Approach.- Ranking-Based Rule Classier Optimisation.- Attribute Selection in a Dispersed Decision-Making System.- Feature Selection Approach for Rule-based Knowledge Bases.- Feature Selection with a Genetic Algorithm for Classication of Brain Imaging Data.<p