TY - GEN
T1 - Redefined Fuzzy Min-Max Neural Network
AU - Wang, Yage
AU - Huang, Wei
AU - Wang, Jinsong
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/7/18
Y1 - 2021/7/18
N2 - The classical fuzzy min-max (FMM) neural network easy to cause the overlap of hyperboxes from different classes, which affect the pattern classification performance. In this paper, we propose a redefined fuzzy min-max (RFMM) neural network to solve this problem. The main contribution is to modify the basic architecture of FMM by adding a redefined hyperbox layer. The proposed RFMM is a four-layer feedforward neural network. The generated hyperbox layer and the redefined hyperbox layer are connected through the proposed hyperbox filter, hyperbox optimization and hyperbox combination. The RFMM learning algorithm is an expansion/contraction/redefinition process. The effectiveness of RFMM is evaluated based on ten benchmarks. Experimental results indicate that RFMM leads to better classification performance than various FMM-based, support vector machine-based models and lower sensitivity to the maximum size of expansion coefficient.
AB - The classical fuzzy min-max (FMM) neural network easy to cause the overlap of hyperboxes from different classes, which affect the pattern classification performance. In this paper, we propose a redefined fuzzy min-max (RFMM) neural network to solve this problem. The main contribution is to modify the basic architecture of FMM by adding a redefined hyperbox layer. The proposed RFMM is a four-layer feedforward neural network. The generated hyperbox layer and the redefined hyperbox layer are connected through the proposed hyperbox filter, hyperbox optimization and hyperbox combination. The RFMM learning algorithm is an expansion/contraction/redefinition process. The effectiveness of RFMM is evaluated based on ten benchmarks. Experimental results indicate that RFMM leads to better classification performance than various FMM-based, support vector machine-based models and lower sensitivity to the maximum size of expansion coefficient.
KW - fuzzy min-max model
KW - hyperbox combination
KW - hyperbox filter
KW - hyperbox optimization
KW - pattern classification
UR - http://www.scopus.com/inward/record.url?scp=85116468554&partnerID=8YFLogxK
U2 - 10.1109/IJCNN52387.2021.9533765
DO - 10.1109/IJCNN52387.2021.9533765
M3 - Conference contribution
AN - SCOPUS:85116468554
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 International Joint Conference on Neural Networks, IJCNN 2021
Y2 - 18 July 2021 through 22 July 2021
ER -