Feature Selection for High-Dimensional Data (pdf)

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Author Verónica Bolón-Canedo, Noelia Sánchez-Maroño, Amparo Alonso-Betanzos (auth.)
Edition 1
Edition Year 2015
Format PDF
ISBN 9783319218571
Language English
Number Of Pages 162
Publisher Springer International Publishing

Description

This book offers a coherent and comprehensive approach to feature subset selection in the scope of classification problems, explaining the foundations, real application problems and the challenges of feature selection for high-dimensional data.

The authors first focus on the analysis and synthesis of feature selection algorithms, presenting a comprehensive review of basic concepts and experimental results of the most well-known algorithms.

They then address different real scenarios with high-dimensional data, showing the use of feature selection algorithms in different contexts with different requirements and information: microarray data, intrusion detection, tear film lipid layer classification and cost-based features. The book then delves into the scenario of big dimension, paying attention to important problems under high-dimensional spaces, such as scalability, distributed processing and real-time processing, scenarios that open up new and interesting challenges for researchers.

The book is useful for practitioners, researchers and graduate students in the areas of machine learning and data mining.

Additional information

Author

Verónica Bolón-Canedo, Noelia Sánchez-Maroño, Amparo Alonso-Betanzos (auth.)

Edition

1

Edition Year

2015

Format

PDF

ISBN

9783319218571

Language

English

Number Of Pages

162

Publisher

Springer International Publishing

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