TY  - BOOK
AU  - Bolón-Canedo,Verónica
AU  - Sánchez-Maroño,Noelia
AU  - Alonso-Betanzos,Amparo
ED  - SpringerLink (Online service)
TI  - Feature Selection for High-Dimensional Data
T2  - Artificial Intelligence: Foundations, Theory, and Algorithms,
SN  - 9783319218588
AV  - Q334-342 
U1  - 006.3 23
PY  - 2015///
CY  - Cham
PB  - Springer International Publishing, Imprint: Springer
KW  - Computer Science
KW  - Data structures (Computer science)
KW  - Data mining
KW  - Artificial intelligence
KW  - Artificial Intelligence (incl. Robotics)
KW  - Data Mining and Knowledge Discovery
KW  - Data Structures
N1  - 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
N2  - 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
UR  - http://dx.doi.org/10.1007/978-3-319-21858-8
ER  -