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008 151005s2015 gw | s |||| 0|eng d
020 _a9783319218588
_z978-3-319-21858-8
024 7 _a10.1007/978-3-319-21858-8
_2doi
040 _aTR-AnTOB
_beng
_cTR-AnTOB
_erda
050 4 _aQ334-342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema006.3
_223
100 1 _aBolón-Canedo, Verónica.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aFeature Selection for High-Dimensional Data /
_cby Verónica Bolón-Canedo, Noelia Sánchez-Maroño, Amparo Alonso-Betanzos.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2015.
300 _a1 online resource
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 0 _aArtificial Intelligence: Foundations, Theory, and Algorithms,
_x2365-3051
505 0 _aIntroduction 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 _aThis 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 _aArtificial intelligence.
650 0 _aData mining.
650 0 _aData structures (Computer scienc.
650 1 4 _aArtificial Intelligence.
_0http://scigraph.springernature.com/things/product-market-codes/I21000
650 2 4 _aData Mining and Knowledge Discovery.
_0http://scigraph.springernature.com/things/product-market-codes/I18030
650 2 4 _aData Structures.
_0http://scigraph.springernature.com/things/product-market-codes/I15017
700 1 _aSánchez-Maroño, Noelia.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aAlonso-Betanzos, Amparo.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
856 4 0 _3Springer eBooks
_zOnline access link to the resource
_uhttps://doi.org/10.1007/978-3-319-21858-8
942 _2lcc
_cEBK
041 _aeng