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

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 in...

全面介紹

Saved in:
書目詳細資料
Main Authors: Leow, S.Y., Wong, S.Y., Yap, K.S., Yap, H.J.
格式: Article
語言:English
出版: 2020
標簽: 添加標簽
沒有標簽, 成為第一個標記此記錄!
實物特徵
總結: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.