TY - JOUR
T1 - Self-Organizing Fuzzy Inference Ensemble System for Big Streaming Data Classification
AU - Gu, Xiaowei
AU - Angelov, Plamen
AU - Zhao, Zhijin
N1 - Funding Information:
This work was partially supported by the National Natural Science Foundation of China ( U19B2016 ).
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/4/22
Y1 - 2021/4/22
N2 - An evolving intelligent system (EIS) is able to self-update its system structure and meta-parameters from streaming data. However, since the majority of EISs are implemented on a single-model architecture, their performances on large-scale, complex data streams are often limited. To address this deficiency, a novel self-organizing fuzzy inference ensemble framework is proposed in this paper. As the base learner of the proposed ensemble system, the self-organizing fuzzy inference system is capable of self-learning a highly transparent predictive model from streaming data on a chunk-by-chunk basis through a human-interpretable process. Very importantly, the base learner can continuously self-adjust its decision boundaries based on the inter-class and intra-class distances between prototypes identified from successive data chunks for higher classification precision. Thanks to its parallel distributed computing architecture, the proposed ensemble framework can achieve great classification precision while maintain high computational efficiency on large-scale problems. Numerical examples based on popular benchmark big data problems demonstrate the superior performance of the proposed approach over the state-of-the-art alternatives in terms of both classification accuracy and computational efficiency.
AB - An evolving intelligent system (EIS) is able to self-update its system structure and meta-parameters from streaming data. However, since the majority of EISs are implemented on a single-model architecture, their performances on large-scale, complex data streams are often limited. To address this deficiency, a novel self-organizing fuzzy inference ensemble framework is proposed in this paper. As the base learner of the proposed ensemble system, the self-organizing fuzzy inference system is capable of self-learning a highly transparent predictive model from streaming data on a chunk-by-chunk basis through a human-interpretable process. Very importantly, the base learner can continuously self-adjust its decision boundaries based on the inter-class and intra-class distances between prototypes identified from successive data chunks for higher classification precision. Thanks to its parallel distributed computing architecture, the proposed ensemble framework can achieve great classification precision while maintain high computational efficiency on large-scale problems. Numerical examples based on popular benchmark big data problems demonstrate the superior performance of the proposed approach over the state-of-the-art alternatives in terms of both classification accuracy and computational efficiency.
KW - Ensemble system
KW - Evolving intelligent system
KW - Large-scale data stream
KW - Prototypes
UR - http://www.scopus.com/inward/record.url?scp=85101392664&partnerID=8YFLogxK
U2 - 10.1016/j.knosys.2021.106870
DO - 10.1016/j.knosys.2021.106870
M3 - Article
SN - 0950-7051
VL - 218
JO - Knowledge-Based Systems
JF - Knowledge-Based Systems
M1 - 106870
ER -