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Feature Selection for High-Dimensional Data [electronic resource] / by Ver�onica Bol�on-Canedo, Noelia S�anchez-Maro�no, Amparo Alonso-Betanzos.

By: Bol�on-Canedo, Ver�onica [author.].
Contributor(s): S�anchez-Maro�no, Noelia [author.] | Alonso-Betanzos, Amparo [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Artificial Intelligence: Foundations, Theory, and Algorithms: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2015Description: XV, 147 p. 16 illus., 8 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319218588.Subject(s): Computer science | Data structures (Computer science) | Data mining | Artificial intelligence | Computer Science | Artificial Intelligence (incl. Robotics) | Data Mining and Knowledge Discovery | Data StructuresAdditional physical formats: Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
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.
In: Springer eBooksSummary: 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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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.

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