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Feature selection for high-dimension...

Alonso-Betanzos, Amparo.

Feature selection for high-dimensional data[electronic resource] /

レコード種別:	言語・文字資料 (印刷物) : 単行資料
[NT 15000414] null:	006.312
タイトル / 著者:	Feature selection for high-dimensional data/ by Veronica Bolon-Canedo, Noelia Sanchez-Marono, Amparo Alonso-Betanzos.
著者:	Bolon-Canedo, Veronica.
その他の著者:	Sanchez-Marono, Noelia.
出版された:	Cham : : Springer International Publishing :, 2015.
記述:	xv, 147 p. : : ill., digital ;; 24 cm.
含まれています:	Springer eBooks
主題:	Data mining.
主題:	Database management.
主題:	Computer Science.
主題:	Artificial Intelligence (incl. Robotics)
主題:	Data Mining and Knowledge Discovery.
主題:	Data Structures.
国際標準図書番号 (ISBN) :	9783319218588
国際標準図書番号 (ISBN) :	9783319218571
[NT 15000228] null:	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.
[NT 15000229] null:	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.
電子資源:	http://dx.doi.org/10.1007/978-3-319-21858-8

Feature selection for high-dimensional data[electronic resource] /
Bolon-Canedo, Veronica.

Feature selection for high-dimensional data[electronic resource] /by Veronica Bolon-Canedo, Noelia Sanchez-Marono, Amparo Alonso-Betanzos. - Cham :Springer International Publishing :2015. - xv, 147 p. :ill., digital ;24 cm. - Artificial intelligence: foundations, theory, and algorithms,2365-3051. - Artificial intelligence: foundations, theory, and algorithms..

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.

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.

ISBN: 9783319218588

Standard No.: 10.1007/978-3-319-21858-8doiSubjects--Topical Terms:

337740
Data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Feature selection for high-dimensional data[electronic resource] /
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245 1 0 $a Feature selection for high-dimensional data $h [electronic resource] / $c by Veronica Bolon-Canedo, Noelia Sanchez-Marono, Amparo Alonso-Betanzos.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2015.
300 $a xv, 147 p. : $b ill., digital ; $c 24 cm.
490 1 $a Artificial intelligence: foundations, theory, and algorithms, $x 2365-3051
505 0 $a 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.
520 $a 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.
650 0 $a Data mining. $3 337740
650 0 $a Database management. $3 174575
650 1 4 $a Computer Science. $3 423143
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 463642
650 2 4 $a Data Mining and Knowledge Discovery. $3 464541
650 2 4 $a Data Structures. $3 467911
700 1 $a Sanchez-Marono, Noelia. $3 634858
700 1 $a Alonso-Betanzos, Amparo. $3 634859
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950 $a Computer Science (Springer-11645)

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http://dx.doi.org/10.1007/978-3-319-21858-8

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