TY - GEN
T1 - Hybrid Ensemble Polynomial Neural Network Classifier
T2 - 27th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2024
AU - Gao, Mingjie
AU - Huang, Wei
AU - Xu, Zhilei
AU - Sungkwun, Oh
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - In this paper, we propose a hybrid ensemble polynomial neural network (HEPNN) with the aid of polynomial neural network (PNN) and hybrid ensemble polynomials neurons (HEPNs). Two types of HEPNs including ensemble radial-based-function polynomial neuron (ERPN) and ensemble polynomial neuron (EPN) are proposed. ERPN and EPN are generalized polynomial neurons based on ensemble architecture. To address the problem of multiple covariance in traditional PNN neural networks, correlation coefficients and performance are utilized to select neurons replacing the original selection of nodes by performance only. The main strategies of HEPNN design are as follows: First, the first layer of the network consists of ERPN that are utilized to reflect the structure encountered between the data, while the second and higher layers consists of EPN, which reflect higher polynomial-order relationships between input and output data. Second, particle swarm optimization (PSO) is adopted to optimize the architecture of HEPNN. A comparative study shows the proposed HEPNN has better performance than other state-of-art models reported in literature.
AB - In this paper, we propose a hybrid ensemble polynomial neural network (HEPNN) with the aid of polynomial neural network (PNN) and hybrid ensemble polynomials neurons (HEPNs). Two types of HEPNs including ensemble radial-based-function polynomial neuron (ERPN) and ensemble polynomial neuron (EPN) are proposed. ERPN and EPN are generalized polynomial neurons based on ensemble architecture. To address the problem of multiple covariance in traditional PNN neural networks, correlation coefficients and performance are utilized to select neurons replacing the original selection of nodes by performance only. The main strategies of HEPNN design are as follows: First, the first layer of the network consists of ERPN that are utilized to reflect the structure encountered between the data, while the second and higher layers consists of EPN, which reflect higher polynomial-order relationships between input and output data. Second, particle swarm optimization (PSO) is adopted to optimize the architecture of HEPNN. A comparative study shows the proposed HEPNN has better performance than other state-of-art models reported in literature.
KW - Hybrid ensemble polynomial neural network (HEPNN)
KW - Particle swarm optimization (PSO)
KW - Polynomial neural network (PNN)
UR - https://www.scopus.com/pages/publications/85199096514
U2 - 10.1109/CSCWD61410.2024.10580808
DO - 10.1109/CSCWD61410.2024.10580808
M3 - Conference contribution
AN - SCOPUS:85199096514
T3 - Proceedings of the 2024 27th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2024
SP - 839
EP - 844
BT - Proceedings of the 2024 27th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2024
A2 - Shen, Weiming
A2 - Shen, Weiming
A2 - Barthes, Jean-Paul
A2 - Luo, Junzhou
A2 - Qiu, Tie
A2 - Zhou, Xiaobo
A2 - Zhang, Jinghui
A2 - Zhu, Haibin
A2 - Peng, Kunkun
A2 - Xu, Tianyi
A2 - Chen, Ning
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 8 May 2024 through 10 May 2024
ER -