A Novel Ensemble Learning Method for Online Learning Scenarios

Liu, Ye; Cao, Weipeng; Liu, Yiwen; Zou, Weidong

doi:10.1109/icet51757.2021.9451004

Cited by 2 publications

(1 citation statement)

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“…Classification problems using ensemble learning have also been studied by several scholars. Ye [19], to alleviate the problem of unstable model performance caused by random input weight of online dequential extreme learning machine (OS‐ELM), an improved OS‐ELM algorithm based on an ensemble learning mechanism is proposed, and the voting mechanism is used to solve this problem. The results show that it is very effective.…”

Section: Related Workmentioning

confidence: 99%

MLfus: A real‐time forecasting architecture for low communication costs in electricity IoT based on ensemble learning

Li¹,

Zhang²,

Li³

et al. 2022

IET Communications

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With the application and popularity of Internet of Things (IoT) technology, real-time prediction of time series data has become the focus of electricity IoT data governance. At present, most of the time-series data prediction methods for the electricity IoT have the defect of being unable to process-related information between sequences. What's worse, the mainstream data fusion methods all have the problem of limited data dimension. This paper proposes a decision-level fusion architecture MLfus for multi-source time-series data generated under the distributed cloud edge structure of the electricity IoT. The model uses ensemble learning to make decisions and judgments on distributed time-series data and integrates multi-source data to make real-time predictions. MLfus solves the problem of significantly biased predictions from a single model and excels in handling complex nonlinear problems. What's more, MLfus can reduce data and additional training requirements substantially by using decision-level fusion. Experimental results show that MLfus has a clear advantage in the problem of real-time electricity price prediction, providing better accuracy while reducing the communication burden.

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Section: Related Workmentioning

confidence: 99%