Abstract
In recent years, an increasing popularity of deep learning models has been widely used in the field of electricity. However, in previous studies, it is always assumed that the training data is sufficient, the training and the testing data are taken from the same feature distribution, which limits their performance on the imbalanced tasks. So, in order to tackle the imbalanced data distribution problem, this paper presents a new model of deep transfer network with balanced distribution adaptation, aiming to adaptively balance the importance of the marginal and conditional distribution discrepancies. By conducting comparative experiments, this model is proved to be effective and have achieved a better performance in both classification accuracy and domain adaptation effectiveness.
This research is supported by the National Key R&D Program of China (No. 2018YFC0831500), the National Social Science Foundation of China (No. 16ZDA055), and the Special Found for Beijing Common Construction Project.
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References
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)
Weiss, K., Khoshgoftaar, T.M., Wang, D.: A survey of transfer learning. J. Big Data 3, 9 (2016)
Long, M., Wang, J., Ding, G., Sun, J., Yu, P.S.: Transfer feature learning with joint distribution adaptation. In: IEEE International Conference on Computer Vision, pp. 2200–2207 (2013)
Pan, S.J., Tsang, I.W., Kwok, J.T., Yang, Q.: Domain adaptation via transfer component analysis. IEEE Trans. Neural Netw. 22(2), 199–210 (2011)
Li, J., Zhao, J., Lu, K.: Joint feature selection and structure preservation for domain adaptation. In: International Joint Conferences on Artificial Intelligence (2016)
Long, M., Wang, J., Ding, G., Sun, J., Yu, P.S.: Transfer joint matching for unsupervised domain adaptation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1410–1417 (2014)
Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks. Eprint Arxiv 27, 3320–3328 (2014)
Long, M., Zhu, H., Wang, J., Jordan, M.I.: Deep transfer learning with joint adaptation networks. In: International Conference on Machine Learning (2016)
Hsu, T.M.H., Chen, W.Y., Hou, C.-A., Tsai, Y.-H.H., Yeh, Y.-R., Wang, Y.-C.F.: Unsupervised domain adaptation with imbalanced cross-domain data. In: IEEE International Conference on Computer Vision, pp. 4121–4129 (2015)
Wang, J., Chen, Y., Hao, S., Feng, W., Shen, Z.: Balanced distribution adaptation for transfer learning. In: IEEE International Conference on Data Mining, pp. 1129–1134 (2017)
Costilla-Reyes, O., Scully, P., Ozanyan, K.B.: Deep neural networks for learning spatio-temporal features from tomography sensors. IEEE Trans. Industr. Electron. 65, 645–653 (2018)
Khatami, A., Babaie, M., Tizhoosh, H.R., Khosravi, A., Nguyen, T., Nahavandi, S.: A sequential search-space shrinking using CNN transfer learning and a Radon projection pool for medical image retrieval. Expert Syst. Appl. 100, 224–233 (2018)
Wei, Y., Zhang, Y., Yang, Q.: Learning to transfer. Eprint arxiv, arXiv:1708.05629 [cs.AI] (2017)
Qureshi, A.S., Khan, A., Zameer, A., Usman, A.: Wind power prediction using deep neural network based meta regression and transfer learning. Appl. Soft Comput. 58, 742–755 (2017)
Lu, W., Liang, B., Cheng, Y., Meng, D., Yang, J., Zhang, T.: Deep model based domain adaptation for fault diagnosis. IEEE Trans. Industr. Electron. 64, 2296–2305 (2017)
Zhang, J., Li, W., Ogunbona, P.: Joint geometrical and statistical alignment for visual domain adaptation. In: IEEE Conference on Computer Vision and Pattern Recognition (2017)
Li, S., Song, S., Huang, G.: Prediction reweighting for domain adaptation. IEEE Trans. Neural Netw. Learn. Syst. 99, 1–14 (2016)
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Wang, K., Wu, B. (2018). Power Equipment Fault Diagnosis Model Based on Deep Transfer Learning with Balanced Distribution Adaptation. In: Gan, G., Li, B., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2018. Lecture Notes in Computer Science(), vol 11323. Springer, Cham. https://doi.org/10.1007/978-3-030-05090-0_16
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DOI: https://doi.org/10.1007/978-3-030-05090-0_16
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