Hierarchical Clustering-Based Synthetic Minority Data Generation for Handling Imbalanced Dataset
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-5180-2_44
Reference18 articles.
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2. Ahmed, S., Mahbub, A., Rayhan, F., Jani, M.R., Shatabda, S., Farid, D.M.: Hybrid methods for class imbalance learning employing bagging with sampling techniques. In: 2nd IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions 2017, 30 August 2018. https://doi.org/10.1109/CSITSS.2017.8447799
3. Sharma, A., Purohit, A., Mishra, H.: A survey on imbalanced data handling techniques for classification. Int. J. Emerg. Trend. Eng. Res. 9(10), 1341–1347 (2021). https://doi.org/10.30534/ijeter/2021/089102021
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5. Barua, S., Islam, M.M., Yao, X., Murase, K.: MWMOTE—majority weighted minority oversampling technique for imbalanced data set learning. IEEE Trans. Knowl. Data Eng. 26(2), 405–425 (2014). https://doi.org/10.1109/TKDE.2012.232
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