Feature Selection for High-Dimensional Data / by Verónica Bolón-Canedo, Noelia Sánchez-Maroño, Amparo Alonso-Betanzos.

Format:

Book

Author/Creator:

Bolón-Canedo, Verónica, author.

Sánchez-Maroño, Noelia, author.

Alonso-Betanzos, Amparo, author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Artificial Intelligence: Foundations, Theory, and Algorithms,. 2365-3051

Artificial Intelligence: Foundations, Theory, and Algorithms, 2365-3051

Language:

English

Subjects (All):

Artificial intelligence.

Data mining.

Data structures (Computer science).

Artificial Intelligence.

Data Mining and Knowledge Discovery.

Data Structures.

Local Subjects:

Artificial Intelligence.

Data Mining and Knowledge Discovery.

Data Structures.

Physical Description:

1 online resource (XV, 147 pages) : 16 illustrations, 8 illustrations in color.

Edition:

First edition 2015.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2015.

System Details:

text file PDF

Summary:

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.

Contents:

Introduction to High-Dimensionality

Foundations of Feature Selection

Experimental Framework

Critical Review of Feature Selection Methods

Application of Feature Selection to Real Problems

Emerging Challenges.

Other Format:

Printed edition:

ISBN:

978-3-319-21858-8

9783319218588

Access Restriction:

Restricted for use by site license.