Feature selection for high-dimensional data

✍ Scribed by Alonso-Betanzos, Amparo; Bolón-Canedo, Verónica; Sánchez-Maroño, Noelia

Publisher: Springer
Year: 2015
Tongue: English
Leaves: 163
Series: Artificial intelligence: foundations theory and algorithms
Edition: 1st ed. 2015
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages i-xv
Introduction to High-Dimensionality....Pages 1-12
Foundations of Feature Selection....Pages 13-28
A Critical Review of Feature Selection Methods....Pages 29-60
Feature Selection in DNA Microarray Classification....Pages 61-94
Application of Feature Selection to Real Problems....Pages 95-124
Emerging Challenges....Pages 125-132
Back Matter....Pages 133-147

✦ Subjects

Artificial Intelligence (incl. Robotics); Data Mining and Knowledge Discovery; Data Structures

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