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Feature Selection for High-Dimensional Data
Verónica Bolón-Canedo
(Author)
·
Noelia Sánchez-Maroño
(Author)
·
Springer
· Paperback
Feature Selection for High-Dimensional Data - Bolón-Canedo, Verónica ; Sánchez-Maroño, Noelia ; Alonso-Betanzos, Amparo
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Synopsis "Feature Selection for High-Dimensional Data"
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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All books in our catalog are Original.
The book is written in English.
The binding of this edition is Paperback.
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