Feature Selection for High-Dimensional Data (Paperback, Softcover reprint of the original 1st ed. 2015)

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.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	Artificial Intelligence: Foundations, Theory, and Algorithms
Release date:	August 2016
First published:	2015
Authors:	Veronica Bolon-Canedo • Noelia Sanchez-Marono • Amparo Alonso-Betanzos
Dimensions:	235 x 155mm (L x W)
Format:	Paperback
Pages:	147
Edition:	Softcover reprint of the original 1st ed. 2015
ISBN-13:	978-3-319-36643-2
Categories:	Books > Computing & IT > General theory of computing > Data structures Books > Computing & IT > Computer programming > Algorithms & procedures Books > Computing & IT > Applications of computing > Databases > Data mining Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN:	3-319-36643-2
Barcode:	9783319366432

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