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A hybrid algorithm of interval type-2 fuzzy logic system and generalized adaptive resonance theory for medical data classification

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dc.citedby1
dc.contributor.authorLeow S.Y.en_US
dc.contributor.authorWong S.Y.en_US
dc.contributor.authorYap K.S.en_US
dc.contributor.authorYap H.J.en_US
dc.contributor.authorid57193235970en_US
dc.contributor.authorid55812054100en_US
dc.contributor.authorid24448864400en_US
dc.contributor.authorid35319362200en_US
dc.date.accessioned2023-05-29T07:30:31Z
dc.date.available2023-05-29T07:30:31Z
dc.date.issued2019
dc.descriptionArts computing; Classification (of information); Computer aided diagnosis; Computer circuits; Diagnosis; Diseases; Functional polymers; Fuzzy inference; Neural networks; Resonance; Uncertainty analysis; Adaptive resonance theory; Adaptive resonance theory networks; Classification tasks; Generalized Regression Neural Network(GRNN); Interval type-2 fuzzy logic systems; Model uncertainties; Regression problem; Type 2 fuzzy sets (T2 FS); Fuzzy logicen_US
dc.description.abstractThe hybrid of artificial neural network (ANN) and fuzzy logic system (FLS) can expend itself dynamically in a strong discovery of explicit knowledge to solve classification and regression problems with new input patterns. In this paper, a hybrid of Generalized Adaptive Resonance Theory (GART) and interval type-2 fuzzy logic system (IT2FLS) algorithm is proposed, and named as Generalized Adaptive Resonance Theory and interval type-2 fuzzy logic system (GART-IT2FLS). The GART is a combination of adaptive resonance theory network (ART) and Generalized Regression Neural Network (GRNN). GART is capable to deal with classification task effectively. However, type-2 fuzzy sets (T2 FS) is used to represent and model the uncertainties on inputs. The performance evaluation of GART-IT2FLS algorithm in three medical datasets has proven that GART-IT2FLS is capable to learn incrementally without plasticity-stability dilemma, and model uncertainties in medical datasets. The inferences of GAR-IT2FLS in these applications are discussed. The performance results show that GART-IT2FLS has obtained a comparable number of rules. The Wisconsin Breast Cancer and Heart Disease experiments demonstrated GART-IT2FLS has improved the testing accuracies. � 2019 - IOS Press and the authors. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.3233/IDT-190358
dc.identifier.epage89
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85064395775
dc.identifier.spage81
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85064395775&doi=10.3233%2fIDT-190358&partnerID=40&md5=e692ded451f7fa7570c49cc2537bbf5c
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25022
dc.identifier.volume13
dc.publisherIOS Pressen_US
dc.sourceScopus
dc.sourcetitleIntelligent Decision Technologies
dc.titleA hybrid algorithm of interval type-2 fuzzy logic system and generalized adaptive resonance theory for medical data classificationen_US
dc.typeArticleen_US
dspace.entity.typePublication
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