Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Soft Computing
Erschienen in: Soft Computing 1/2020

21.06.2019 | Focus

Toward a development of general type-2 fuzzy classifiers applied in diagnosis problems through embedded type-1 fuzzy classifiers

verfasst von: Emanuel Ontiveros-Robles, Patricia Melin

Erschienen in: Soft Computing | Ausgabe 1/2020

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Nowadays, with the emergence of computer-aided systems, diagnosis problems are one of the most important application areas of artificial intelligence. The present paper is focused on a specific kind of computer-aided diagnosis system based on General Type-2 Fuzzy Logic. The main goal is the generation of General Type-2 Fuzzy Classifiers that can handle the data uncertainty. The concept of embedded Type-1 Fuzzy membership functions has been proposed to be used in the design of General Type-2 Fuzzy Classifiers. A methodology for generating the embedded Type-1 fuzzy membership functions is introduced, and the subsequent approach for developing the Footprint of Uncertainty of the General Type-2 Fuzzy Classifier is presented. On the other hand, the proposed approach performance is evaluated by the experimentation with different diagnosis benchmark problems. In addition, a statistical comparison with respect to another existing approach of General Type-2 Fuzzy classifiers is presented.
Vorheriger Artikel An optimal redistribution plan considering aftermath disruption in disaster management
Nächster Artikel Improved grey wolf optimization-based feature subset selection with fuzzy neural classifier for financial crisis prediction

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
Acharya UR, Oh SL, Hagiwara Y et al (2018) Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput Biol Med 100:270–278CrossRef
Afifi S, GholamHosseini H, Sinha R (2019) A system on chip for melanoma detection using FPGA-based SVM classifier. Microprocess Microsyst 65:57–68CrossRef
Asl AAS, Zarandi MHF (2017) A type-2 fuzzy expert system for diagnosis of leukemia. In: Melin P, Castillo O, Kacprzyk J, Reformat M, Melek W (eds) Fuzzy logic in intelligent system design. Springer, Cham, pp 52–60
Babapour Mofrad R, Schoonenboom NSM, Tijms BM et al (2019) Decision tree supports the interpretation of CSF biomarkers in Alzheimer’s disease. Alzheimers Dement Diagn Assess Dis Monit 11:1–9
Baraldi P, Razavi-Far R, Zio E (2011) Bagged ensemble of fuzzy C-means classifiers for nuclear transient identification. Ann Nucl Energy 38:1161–1171CrossRef
Caraveo C, Valdez F, Castillo O (2016) Optimization of fuzzy controller design using a new bee colony algorithm with fuzzy dynamic parameter adaptation. Appl Soft Comput 43:131–142CrossRef
Castillo O, Amador-Angulo L, Castro JR, Garcia-Valdez M (2016) A comparative study of type-1 fuzzy logic systems, interval type-2 fuzzy logic systems and generalized type-2 fuzzy logic systems in control problems. Inf Sci 354:257–274CrossRef
Davari Dolatabadi A, Khadem SEZ, Asl BM (2017) Automated diagnosis of coronary artery disease (CAD) patients using optimized SVM. Comput Methods Programs Biomed 138:117–126CrossRef
Elyan E, Gaber MM (2016) A fine-grained Random Forests using class decomposition: an application to medical diagnosis. Neural Comput Appl 27:2279–2288CrossRef
Erkaymaz O, Ozer M (2016) Impact of small-world network topology on the conventional artificial neural network for the diagnosis of diabetes. Chaos, Solitons Fractals 83:178–185MathSciNetCrossRef
Fernández-Carrobles MM, Serrano I, Bueno G, Déniz O (2016) Bagging tree classifier and texture features for tumor identification in histological images. Procedia Comput Sci 90:99–106CrossRef
Froelich W (2017) Towards improving the efficiency of the fuzzy cognitive map classifier. Neurocomputing 232:83–93CrossRef
Fu C, Lu W, Pedrycz W, Yang J (2019) Fuzzy granular classification based on the principle of justifiable granularity. Knowl Based Syst 170:89–101CrossRef
Hagras H (2008) Developing a type-2 FLC through embedded type-1 FLCs. In: 2008 IEEE international conference on fuzzy systems (IEEE world congress on computational intelligence). IEEE, Hong Kong, China, pp 148–155
Hu X, Pedrycz W, Wang X (2018) Fuzzy classifiers with information granules in feature space and logic-based computing. Pattern Recognit 80:156–167CrossRef
Jang J-SR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685CrossRef
Lahsasna A, Seng WC (2017) An improved genetic-fuzzy system for classification and data analysis. Expert Syst Appl 83:49–62CrossRef
Liang Q, Mendel JM (2000) Interval type-2 fuzzy logic systems: theory and design. IEEE Trans Fuzzy Syst 8:535–550CrossRef
Liao Q, Ding Y, Jiang ZL et al (2018) Multi-task deep convolutional neural network for cancer diagnosis. Neurocomputing 348:66–73CrossRef
Melin P, Gonzalez CI, Castro JR et al (2014) Edge-detection method for image processing based on generalized type-2 fuzzy logic. IEEE Trans Fuzzy Syst 22:1515–1525CrossRef
Mendel JM, John RIB (2002) Type-2 fuzzy sets made simple. IEEE Trans Fuzzy Syst 10:117–127CrossRef
Mendel JM, John RI, Liu F (2006) Interval type-2 fuzzy logic systems made simple. IEEE Trans Fuzzy Syst 14:808–821CrossRef
Mendel JM, Liu F, Zhai D (2009) α-plane representation for type-2 fuzzy sets: theory and applications. IEEE Trans Fuzzy Syst 17:1189–1207CrossRef
Mendel JM, Rajati MR, Sussner P (2016) On clarifying some definitions and notations used for type-2 fuzzy sets as well as some recommended changes. Inf Sci 340–341:337–345MathSciNetCrossRef
Ontiveros E, Melin P, Castillo O (2018a) Impact study of the footprint of uncertainty in control applications based on interval type-2 fuzzy logic controllers. In: Castillo O, Melin P, Kacprzyk J (eds) Fuzzy logic augmentation of neural and optimization algorithms: theoretical aspects and real applications. Springer, Cham, pp 181–197
Ontiveros E, Melin P, Castillo O (2018b) High order α-planes integration: a new approach to computational cost reduction of general type-2 fuzzy systems. Eng Appl Artif Intell 74:186–197CrossRef
Peraza C, Valdez F, Melin P (2017) Optimization of intelligent controllers using a type-1 and interval type-2 fuzzy harmony search algorithm. Algorithms 10:82MathSciNetCrossRef
Pota M, Esposito M, De Pietro G (2018) Likelihood-fuzzy analysis: from data, through statistics, to interpretable fuzzy classifiers. Int J Approx Reason 93:88–102MathSciNetCrossRef
Qi X, Zhang L, Chen Y et al (2019) Automated diagnosis of breast ultrasonography images using deep neural networks. Med Image Anal 52:185–198CrossRef
Rakhmetulayeva SB, Duisebekova KS, Mamyrbekov AM et al (2018) Application of classification algorithm based on SVM for determining the effectiveness of treatment of tuberculosis. Procedia Comput Sci 130:231–238CrossRef
Sanchez MA, Castro JR, Ocegueda-Miramontes V, Cervantes L (2017) Hybrid learning for general type-2 TSK fuzzy logic systems. Algorithms 10:99CrossRef
Saritas I (2012) Prediction of breast cancer using artificial neural networks. J Med Syst 36:2901–2907CrossRef
Subasi A (2013) Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders. Comput Biol Med 43:576–586CrossRef
Vogado LHS, Veras RMS, FlavioHD Araujo et al (2018) Leukemia diagnosis in blood slides using transfer learning in CNNs and SVM for classification. Eng Appl Artif Intell 72:415–422CrossRef
Wang H, Zheng B, Yoon SW, Ko HS (2018) A support vector machine-based ensemble algorithm for breast cancer diagnosis. Eur J Oper Res 267:687–699MathSciNetCrossRef
Wu D, Tan WW (2005) Computationally efficient type-reduction strategies for a type-2 fuzzy logic controller. In: The 14th IEEE international conference on fuzzy systems, 2005. FUZZ’05, pp 353–358
Metadaten
Titel
Toward a development of general type-2 fuzzy classifiers applied in diagnosis problems through embedded type-1 fuzzy classifiers
verfasst von
Emanuel Ontiveros-Robles
Patricia Melin
Publikationsdatum
21.06.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 1/2020
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-019-04157-2

Weitere Artikel der Ausgabe 1/2020

Soft Computing 1/2020 Zur Ausgabe

Foundations

Quantile fuzzy regression based on fuzzy outputs and fuzzy parameters

Foundations

A space–time trade-off for computing the visibility polygon in the multi-pass model

Focus

Multi-objective optimization of cost-effective and customer-centric closed-loop supply chain management model in T-environment

Methodologies and Application

Optimal feature selection in industrial foam injection processes using hybrid binary Particle Swarm Optimization and Gravitational Search Algorithm in the Mahalanobis–Taguchi System

Methodologies and Application

A topological method for reduction in digital information uncertainty

Methodologies and Application

Some solving methods for a fuzzy multi-point boundary value problem

Premium Partner

    Bildnachweise