@inproceedings{2c2137a39c634a87bbe8fb723aac9896,
title = "Research on diagnosis method based on multi-class sample imbalanced data",
abstract = "In reality, data on many engineering problems are classified as imbalanced data, which poses significant challenges to adopting a data-driven approach. In this paper, the SOU data preprocessing method combining synthetic minority oversampling technique, random oversampling and random under sampling is used for data equalization. After, the cars evaluation, wine production regions are classified using the ensemble learning based on stacking method. Research is also conducted on the classification of imbalances in the condition assessment of aero-engines at work. The results show that the data equalization method and diagnostic framework proposed in this paper can effectively diagnose multi-class imbalanced data, and the ensemble model has higher diagnostic stability and generalization ability, which is more effective for solving multi-class imbalance problems.",
keywords = "Ensemble learning, Health status diagnosis, Multi-class imbalanced data, SOU method",
author = "Yuanyuan Li and Xinlei Wang and Chenhui Ren and Yingshun Li and Peng Hou and Xiaojian Yi",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019 ; Conference date: 15-08-2019 Through 17-08-2019",
year = "2019",
month = aug,
doi = "10.1109/SDPC.2019.00156",
language = "English",
series = "Proceedings - 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "821--825",
editor = "Chuan Li and Shaohui Zhang and Jianyu Long and Diego Cabrera and Ping Ding",
booktitle = "Proceedings - 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019",
address = "United States",
}