Ensemble learning methodologies to improve core power distribution abnormal detectability

Li, Wenhuai; Ding, Peng; Zhang, Xiangju; Duan, Chengjie; Qiu, Ruoxiang; Lin, Jiming; Shi, Xiuan

doi:10.1016/j.nucengdes.2019.06.004

Cited by 10 publications

(1 citation statement)

References 13 publications

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“…The ensemble learning models can fuse a variety of prediction results from different individual models for collaborative decision-making, enabling a more accurate, stable, and robust final result. At present, the commonly used fusion strategies are the voting, averaging, and learning methods [15][16][17][18]. In recent years, many studies have shown that the performance of an ensemble model can be effectively improved by improving the strategy of fusing the individual models [19][20][21].…”

Section: Related Workmentioning

confidence: 99%

Prediction of Repeat Customers on E-Commerce Platform Based on Blockchain

Zhang

Dong

2020

Wireless Communications and Mobile Computing

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In recent years, blockchain has substantially enhanced the credibility of e-commerce platforms for users. The prediction accuracy of the repeat purchase behaviour of e-commerce users directly affects the impact of precision marketing by merchants. The existing ensemble learning models have low prediction accuracy when the purchase behaviour sample is unbalanced and the information dimension of feature engineering is single. To overcome this problem, an ensemble learning prediction model based on multisource information fusion is proposed. Tests on the Tmall dataset showed that the accuracy and AUC values of the model reached 91.28% and 70.53%, respectively.

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