000			03220nam a22003615i 4500
001			102349
003			DE-He213
005			20240111050138.0
007			cr nn 008mamaa
008			180430s2018 gw | s |||| 0|eng d
020			_a9783319900803
024	7		_a10.1007/978-3-319-90080-3 _2doi
040			_aES-MaUEC _bspa
050		4	_aQ342 _b2018 EB
100	1		_aBolón-Canedo, Verónica _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _0http://id.loc.gov/authorities/names/no2016000437 _1http://viaf.org/viaf/123145541779096601976/
245	1	0	_aRecent Advances in Ensembles for Feature Selection _cby Verónica Bolón-Canedo, Amparo Alonso-Betanzos.
264		1	_aCham _bSpringer International Publishing _c2018
300			_a1 recurso en línea (XIV, 205 páginas 39 ilustraciones, 36 ilustraciones a color)
347			_atext file _bPDF
490	0		_aIntelligent Systems Reference Library _x1868-4394 _v147
505	0		_aBasic concepts -- Feature selection -- Foundations of ensemble learning -- Ensembles for feature selection -- Combination of outputs -- Evaluation of ensembles for feature selection -- Other ensemble approaches -- Applications of ensembles versus traditional approaches: experimental results -- Software tools -- Emerging Challenges. .
520	3		_aThis book offers a comprehensive overview of ensemble learning in the field of feature selection (FS), which consists of combining the output of multiple methods to obtain better results than any single method. It reviews various techniques for combining partial results, measuring diversity and evaluating ensemble performance. With the advent of Big Data, feature selection (FS) has become more necessary than ever to achieve dimensionality reduction. With so many methods available, it is difficult to choose the most appropriate one for a given setting, thus making the ensemble paradigm an interesting alternative. The authors first focus on the foundations of ensemble learning and classical approaches, before diving into the specific aspects of ensembles for FS, such as combining partial results, measuring diversity and evaluating ensemble performance. Lastly, the book shows examples of successful applications of ensembles for FS and introduces the new challenges that researchers now face. As such, the book offers a valuable guide for all practitioners, researchers and graduate students in the areas of machine learning and data mining. .
650		7	_9666321 _aIngeniería asistida por ordenador
650		7	_aInteligencia artificial _2embne _9413115
650		7	_aReconocimiento de formas _2embne _9152614
700	1		_aAlonso-Betanzos, Amparo _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _0http://id.loc.gov/authorities/names/no2009008642 _1http://viaf.org/viaf/21977156/
776	0	8	_iEdición impresa: _z9783319900797
776	0	8	_iEdición impresa: _z9783319900810
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-90080-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
490	0		_aEngineering (Springer-11647)
988			_aEBSPRINGER_2018
998			_b12/2018 _dz _ea _feng _ggw _h0
999			_c102349 _d102349 _x1