Feature Selection for Data and Pattern Recognition

✍ Scribed by Urszula Stańczyk, Lakhmi C. Jain (eds.)

Publisher: Springer-Verlag Berlin Heidelberg
Year: 2015
Tongue: English
Leaves: 362
Series: Studies in Computational Intelligence 584
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages i-xviii
Feature Selection for Data and Pattern Recognition: An Introduction....Pages 1-7
Front Matter....Pages 9-9
All Relevant Feature Selection Methods and Applications....Pages 11-28
Feature Evaluation by Filter, Wrapper, and Embedded Approaches....Pages 29-44
A Geometric Approach to Feature Ranking Based Upon Results of Effective Decision Boundary Feature Matrix....Pages 45-69
Weighting of Features by Sequential Selection....Pages 71-90
Front Matter....Pages 91-91
Dependency Analysis and Attribute Reduction in the Probabilistic Approach to Rough Sets....Pages 93-111
Structure-Based Attribute Reduction: A Rough Set Approach....Pages 113-160
Front Matter....Pages 161-161
A Comparison of Rule Induction Using Feature Selection and the LEM2 Algorithm....Pages 163-176
Meta-actions as a Tool for Action Rules Evaluation....Pages 177-197
Irrelevant Feature and Rule Removal for Structural Associative Classification Using Structure-Preserving Flat Representation....Pages 199-228
Front Matter....Pages 229-229
Hubness-Aware Classification, Instance Selection and Feature Construction: Survey and Extensions to Time-Series....Pages 231-262
Selection of Visual Descriptors for the Purpose of Multi-camera Object Re-identification....Pages 263-303
Improving the Recognition Performance of Moment Features by Selection....Pages 305-327
Signature Selection for Grouped Features with a Case Study on Exon Microarrays....Pages 329-349
Back Matter....Pages 351-355

✦ Subjects

Computational Intelligence; Artificial Intelligence (incl. Robotics)

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