Rights statement: ©2020 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Accepted author manuscript, 553 KB, PDF document
Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Multi-Layer Ensemble Evolving Fuzzy Inference System
AU - Gu, Xiaowei
N1 - ©2020 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
PY - 2021/8/31
Y1 - 2021/8/31
N2 - In order to tackle high-dimensional, complex problems, learning models have to go deeper. In this paper, a novel multi-layer ensemble learning model with firrst-order evolving fuzzy systems as its building blocks is introduced. The proposed approach can effectively learn from streaming data on a sample-by-sample basis and self-organizes its multi-layered system structure and meta-parameters in a feed-forward, non-iterative manner. Benefiting from its multi-layered distributed representation learning ability, the ensemble system not only demonstrates the state-of-the-art performance on various problems, but also offers high level of system transparency and explainability. Theoretical justifications and experimental investigation show the validity and effectiveness of the proposed concept and general principles.
AB - In order to tackle high-dimensional, complex problems, learning models have to go deeper. In this paper, a novel multi-layer ensemble learning model with firrst-order evolving fuzzy systems as its building blocks is introduced. The proposed approach can effectively learn from streaming data on a sample-by-sample basis and self-organizes its multi-layered system structure and meta-parameters in a feed-forward, non-iterative manner. Benefiting from its multi-layered distributed representation learning ability, the ensemble system not only demonstrates the state-of-the-art performance on various problems, but also offers high level of system transparency and explainability. Theoretical justifications and experimental investigation show the validity and effectiveness of the proposed concept and general principles.
KW - Ensemble model
KW - Evolving fuzzy system
KW - multilayered structure
KW - Transparency
U2 - 10.1109/TFUZZ.2020.2988846
DO - 10.1109/TFUZZ.2020.2988846
M3 - Journal article
VL - 29
SP - 2425
EP - 2431
JO - IEEE Transactions on Fuzzy Systems
JF - IEEE Transactions on Fuzzy Systems
SN - 1063-6706
IS - 8
ER -