000			03304nam a22004935i 4500
001			978-3-319-41111-8
003			DE-He213
005			20200421112042.0
007			cr nn 008mamaa
008			160809s2016 gw | s |||| 0|eng d
020			_a9783319411118 _9978-3-319-41111-8
024	7		_a10.1007/978-3-319-41111-8 _2doi
050		4	_aQA76.9.D343
072		7	_aUNF _2bicssc
072		7	_aUYQE _2bicssc
072		7	_aCOM021030 _2bisacsh
082	0	4	_a006.312 _223
100	1		_aHerrera, Francisco. _eauthor.
245	1	0	_aMultilabel Classification _h[electronic resource] : _bProblem Analysis, Metrics and Techniques / _cby Francisco Herrera, Francisco Charte, Antonio J. Rivera, Mar�ia J. del Jesus.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2016.
300			_aXVI, 194 p. 72 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aIntroduction -- Multilabel Classification -- Case Studies and Metrics -- Transformation based Classifiers -- Adaptation based Classifiers -- Ensemble based Classifiers -- Dimensionality Reduction -- Imbalance in Multilabel Datasets -- Multilabel Software.
520			_aThis book offers a comprehensive review of multilabel techniques widely used to classify and label texts, pictures, videos and music in the Internet. A deep review of the specialized literature on the field includes the available software needed to work with this kind of data. It provides the user with the software tools needed to deal with multilabel data, as well as step by step instruction on how to use them. The main topics covered are: • The special characteristics of multi-labeled data and the metrics available to measure them. • The importance of taking advantage of label correlations to improve the results. • The different approaches followed to face multi-label classification. • The preprocessing techniques applicable to multi-label datasets. • The available software tools to work with multi-label data. This book is beneficial for professionals and researchers in a variety of fields because of the wide range of potential applications for multilabel classification. Besides its multiple applications to classify different types of online information, it is also useful in many other areas, such as genomics and biology. No previous knowledge about the subject is required. The book introduces all the needed concepts to understand multilabel data characterization, treatment and evaluation.
650		0	_aComputer science.
650		0	_aData mining.
650		0	_aArtificial intelligence.
650	1	4	_aComputer Science.
650	2	4	_aData Mining and Knowledge Discovery.
650	2	4	_aArtificial Intelligence (incl. Robotics).
700	1		_aCharte, Francisco. _eauthor.
700	1		_aRivera, Antonio J. _eauthor.
700	1		_adel Jesus, Mar�ia J. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783319411101
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-319-41111-8
912			_aZDB-2-SCS
942			_cEBK
999			_c56670 _d56670