An Ensemble Fuzziness-Based Online Sequential Learning Approach and Its Application

Cao，Wei Peng1; Li，Sheng Dong2; Huang，Cheng Chao3; Wu，Yu Hao1; Wang，Qiang4; Li，Da Chuan4; Liu，Ye1

10.1007/978-3-030-82136-4_21

2021

0302-9743

1611-3349

LECT NOTES ARTIF INT

12815 LNAI

255-267

Traditional deep learning algorithms are difficult to deploy on most IoT terminal devices due to their limited computing power. To solve this problem, this paper proposes a novel ensemble fuzziness-based online sequential learning approach to support the local update of terminal intelligent models and improve their prediction performance. Our method consists of two modules: server module and terminal module. The latter uploads the data collected in real-time to the server module, then the server module selects the most valuable samples and sends them back to the terminal module for the local update. Specifically, the server module uses the ensemble learning mechanism to filter data through multiple fuzzy classifiers, while the terminal module uses the online neural networks with random weights to update the local model. Extensive experimental results on ten benchmark data sets show that the proposed method outperforms other similar algorithms in prediction. Moreover, we apply the proposed method to solve the network intrusion detection problem, and the corresponding experimental results show that our method has better generalization ability than other existing solutions.

Ensemble learning Extreme learning machine Fuzzy classifier Intrusion detection system Online learning

其他

英语

CPCI-S ; EI

WOS:000689138600021

20213510835233

Deep learning ; E-learning ; Fuzzy filters ; Fuzzy set theory ; Intrusion detection ; Learning systems

Computer Software, Data Handling and Applications:723 ; Combinatorial Mathematics, Includes Graph Theory, Set Theory:921.4

2-s2.0-85113725174

Scopus

1.College of Computer Science and Software Engineering,Shenzhen University,Shenzhen,China
2.China Electronics Cloud Brain (Tianjin) Technology CO.,LTD.,Tianjin,China
3.Nanjing Institute of Software Technology,ISCAS,Nanjing,China
4.Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,China

Cao，Wei Peng,Li，Sheng Dong,Huang，Cheng Chao,et al. An Ensemble Fuzziness-Based Online Sequential Learning Approach and Its Application[C],2021:255-267.