000			03323nam a22005415i 4500
001			978-3-319-21858-8
003			DE-He213
005			20200421112231.0
007			cr nn 008mamaa
008			151005s2015 gw | s |||| 0|eng d
020			_a9783319218588 _9978-3-319-21858-8
024	7		_a10.1007/978-3-319-21858-8 _2doi
050		4	_aQ334-342
050		4	_aTJ210.2-211.495
072		7	_aUYQ _2bicssc
072		7	_aTJFM1 _2bicssc
072		7	_aCOM004000 _2bisacsh
082	0	4	_a006.3 _223
100	1		_aBol�on-Canedo, Ver�onica. _eauthor.
245	1	0	_aFeature Selection for High-Dimensional Data _h[electronic resource] / _cby Ver�onica Bol�on-Canedo, Noelia S�anchez-Maro�no, Amparo Alonso-Betanzos.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2015.
300			_aXV, 147 p. 16 illus., 8 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_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	_aComputer science.
650		0	_aData structures (Computer science).
650		0	_aData mining.
650		0	_aArtificial intelligence.
650	1	4	_aComputer Science.
650	2	4	_aArtificial Intelligence (incl. Robotics).
650	2	4	_aData Mining and Knowledge Discovery.
650	2	4	_aData Structures.
700	1		_aS�anchez-Maro�no, Noelia. _eauthor.
700	1		_aAlonso-Betanzos, Amparo. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783319218571
830		0	_aArtificial Intelligence: Foundations, Theory, and Algorithms, _x2365-3051
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-319-21858-8
912			_aZDB-2-SCS
942			_cEBK
999			_c57968 _d57968