Feature Selection for Data and Pattern Recognition

This research book provides the reader with a selection of high-quality texts dedicated to current progress, new developments and research trends in feature selection for data and pattern recognition.

Even though it has been the subject of interest for some time, feature selection remains one of actively pursued avenues of investigations due to its importance and bearing upon other problems and tasks.

This volume points to a number of advances topically subdivided into four parts: estimation of importance of characteristic features, their relevance, dependencies, weighting and ranking; rough set approach to attribute reduction with focus on relative reducts; construction of rules and their evaluation; and data- and domain-oriented methodologies.

Recent research trends in feature selection for data and pattern recognition Points to a number of advances topically subdivided into four parts: estimation of importance of characteristic features, their relevance, dependencies, weighting and ranking; rough set approach to attribute reduction with focus on relative reducts; construction of rules and their evaluation; and data- and domain-oriented methodologies Presents approaches in feature selection for data and pattern classification using computational intelligence paradigms Includes supplementary material: sn.pub/extras

Inhalt
Feature Selection for Data and Pattern Recogniton: an Introduction.- Part I Estimation of Feature Importance.- Part II Rough Set Approach to Attribute Reduction.- Part III Rule Discovery and Evaluation.- Part IV Data- and Domain-oriented Methodologies.