Publication: A hybrid algorithm of interval type-2 fuzzy logic system and generalized adaptive resonance theory for medical data classification
No Thumbnail Available
Date
2019
Authors
Leow S.Y.
Wong S.Y.
Yap K.S.
Yap H.J.
Journal Title
Journal ISSN
Volume Title
Publisher
IOS Press
Abstract
The 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.
Description
Arts 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 logic