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Feature Selection for High-Dimensional Data

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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.

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Detail Information

Series Title

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Call Number

XV, 147

Publisher

: Springer Cham., 2015

Collation

-

Language

English

ISBN/ISSN

978-3-319-21858-8

Classification

NONE

Content Type

text

Media Type

computer

Carrier Type

online resource

Edition

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Subject(s)

Data Science
Artificial Intelligence
Data Mining and Knowledge Discovery

Specific Detail Info

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Statement of Responsibility

Verónica Bolón-Canedo, Noelia Sánchez-Maroño, Amparo Alonso-Betanzos

Other Information

Cataloger

agus

Source

-

Validator

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