LEADER 04152nam 22007215 450 001 9910484712303321 005 20251113210203.0 010 $a3-030-75097-3 024 7 $a10.1007/978-3-030-75097-8 035 $a(CKB)4100000011955301 035 $a(MiAaPQ)EBC6637100 035 $a(Au-PeEL)EBL6637100 035 $a(OCoLC)1255466534 035 $z(PPN)258861967 035 $a(PPN)258087943 035 $a(DE-He213)978-3-030-75097-8 035 $a(EXLCZ)994100000011955301 100 $a20210603d2021 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aNew Medical Diagnosis Models Based on Generalized Type-2 Fuzzy Logic /$fby Patricia Melin, Emanuel Ontiveros-Robles, Oscar Castillo 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (85 pages) 225 1 $aSpringerBriefs in Computational Intelligence,$x2625-3712 311 08$a3-030-75096-5 327 $aIntroduction -- Background and theory -- Proposed Methodology -- Experimental Results -- Results discussion -- Conclusions. 330 $aThis book presents different experimental results as evidence of the good results obtained compared with respect to conventional approaches and literature references based on fuzzy logic. Nowadays, the evolution of intelligence systems for decision making has been reached considerable levels of success, as these systems are getting more intelligent and can be of great help to experts in decision making. One of the more important realms in decision making is the area of medical diagnosis, and many kinds of intelligence systems provide the expert good assistance to perform diagnosis; some of these methods are, for example, artificial neural networks (can be very powerful to find tendencies), support vector machines, that avoid overfitting problems, and statistical approaches (e.g., Bayesian). However, the present research is focused on one of the most relevant kinds of intelligent systems, which are the fuzzy systems. The main objective of the present work is the generation of fuzzy diagnosis systems that offer competitive classifiers to be applied in diagnosis systems. To generate these systems, we have proposed a methodology for the automatic design of classifiers and is focused in the Generalized Type-2 Fuzzy Logic, because the uncertainty handling can provide us with the robustness necessary to be competitive with other kinds of methods. In addition, different alternatives to the uncertainty modeling, rules-selection, and optimization have been explored. Besides, different experimental results are presented as evidence of the good results obtained when compared with respect to conventional approaches and literature references based on Fuzzy Logic. 410 0$aSpringerBriefs in Computational Intelligence,$x2625-3712 606 $aComputational intelligence 606 $aElectronic circuits 606 $aMathematics 606 $aSignal processing 606 $aComputer science 606 $aComputational Intelligence 606 $aElectronic Circuits and Systems 606 $aApplications of Mathematics 606 $aDigital and Analog Signal Processing 606 $aTheory of Computation 615 0$aComputational intelligence. 615 0$aElectronic circuits. 615 0$aMathematics. 615 0$aSignal processing. 615 0$aComputer science. 615 14$aComputational Intelligence. 615 24$aElectronic Circuits and Systems. 615 24$aApplications of Mathematics. 615 24$aDigital and Analog Signal Processing. 615 24$aTheory of Computation. 676 $a006.3 700 $aMelin$b Patricia$f1962-$0762263 702 $aOntiveros-Robles$b Emanuel 702 $aCastillo$b Oscar 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484712303321 996 $aNew medical diagnosis models based on generalized type-2 fuzzy logic$92585801 997 $aUNINA