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008			210603s2021 sz | s |||| 0|eng d
020			_a9783030750978
024	7		_a10.1007/978-3-030-75097-8 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aRC71.3 _b2021 EB
100	1		_aMelin, Patricia _d1962- _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _997926
245	1	0	_aNew Medical Diagnosis Models Based on Generalized Type-2 Fuzzy Logic _cby Patricia Melin, Emanuel Ontiveros-Robles, Oscar Castillo.
250			_aFirst edition 2021
264		1	_aCham _bSpringer International Pulishing _c2021
300			_a1 recurso en línea (VIII, 78 páginas) _b76 ilustraciones, 74 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aSpringerBriefs in Computational Intelligence _x2625-3712
490	0		_aIntelligent Technologies and Robotics (SpringerNature-42732)
490	0		_aIntelligent Technologies and Robotics (R0) (SpringerNature-43728)
505	0		_aIntroduction -- Background and theory -- Proposed Methodology -- Experimental Results -- Results discussion -- Conclusions.
520	3		_aThis book presents different experimental results as evidence of the good results obtained compared with respect to conventional approaches and literature references based on fuzzy logic. Nowadays, the evolution of intelligence systems for decision making has been reached considerable levels of success, as these systems are getting more intelligent and can be of great help to experts in decision making. One of the more important realms in decision making is the area of medical diagnosis, and many kinds of intelligence systems provide the expert good assistance to perform diagnosis; some of these methods are, for example, artificial neural networks (can be very powerful to find tendencies), support vector machines, that avoid overfitting problems, and statistical approaches (e.g., Bayesian). However, the present research is focused on one of the most relevant kinds of intelligent systems, which are the fuzzy systems. The main objective of the present work is the generation of fuzzy diagnosis systems that offer competitive classifiers to be applied in diagnosis systems. To generate these systems, we have proposed a methodology for the automatic design of classifiers and is focused in the Generalized Type-2 Fuzzy Logic, because the uncertainty handling can provide us with the robustness necessary to be competitive with other kinds of methods. In addition, different alternatives to the uncertainty modeling, rules-selection, and optimization have been explored. Besides, different experimental results are presented as evidence of the good results obtained when compared with respect to conventional approaches and literature references based on Fuzzy Logic.
988			_aSpringer_Robotics_2021
650		7	_2embne _aToma de decisiones _9141176
700			_aOntiveros-Robles, Emanuel _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9682463
700	1		_aCastillo, Óscar _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _937054
776	0	8	_iPrinted edition: _z9783030750961
776	0	8	_iPrinted edition: _z9783030750985
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-75097-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE