TY  - JOUR
N2  - This paper develops a hybrid approach integrating an adaptive artificial neural network (ANN) and a fuzzy logic system for tackling class-imbalance problems. In particular, a supervised learning ANN based on Adaptive Resonance Theory (ART) is combined with a Tagakiâ??Sugenoâ??Kang-based fuzzy inference mechanism to learn and detect defects of a real large highly imbalanced dataset collected from a semiconductor company. A benchmark study is also conducted to compare the classification performance of the proposed method with other published methods in the literature. The real dataset collected from the semiconductor company intrinsically demonstrates class overlap and data shift in a highly imbalanced data environment. The generalization ability of the proposed method in detecting semiconductor defects is evaluated and compared with other existing methods, and the results are analyzed using statistical methods. The outcomes from the empirical studies positively indicate high potentials of the proposed approach in classifying the highly imbalanced dataset posing overlap class and data shift. © 2017
KW  - Benchmarking; Classification (of information); Defects; Fuzzy inference; Neural networks
KW  -  Adaptive artificial neural networks; Adaptive resonance theory; Class imbalance; Class overlap; Classification performance; Data shift; Fuzzy inference mechanism; Inference mechanism
KW  -  Fuzzy logic
ID  - scholars10545
Y1  - 2018///
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032186655&doi=10.1016%2fj.ins.2017.10.040&partnerID=40&md5=fefe70c2341a3f89d2d12059cf151c29
JF  - Information Sciences
A1  - Tan, S.C.
A1  - Wang, S.
A1  - Watada, J.
VL  - 427
AV  - none
N1  - cited By 9
TI  - A self-adaptive class-imbalance TSK neural network with applications to semiconductor defects detection
SP  - 1
SN  - 00200255
PB  - Elsevier Inc.
EP  - 17
ER  -