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- 解决数据不平衡问题-SMOTE算法 - 知乎
未来，如何更加智能地合成少数类样本、如何更好地与其他机器学习算法相结合，以及如何在大数据环境下高效应用 SMOTE 等问题，都将是研究的重点方向。 通过不断的研究和创新，SMOTE 有望在解决数据不平衡问题、推动机器学习技术发展方面发挥更大的作用。
- 【机器学习】合成少数过采样技术 (SMOTE)处理不平衡数据（附代码）_smote过采样-CSDN博客
其中， 合成少数过采样技术 （SMOTE）是一种常用的 技术，它通过对 少数 类的样本进行插值，生成新的 合成 样本，以此来减少类别间的 不平衡。 在多类别分类问题中， SMOTE 可以有选择性地应用于那些样本量较少的类别，以此来
- [1106. 1813] SMOTE: Synthetic Minority Over-sampling Technique
This paper shows that a combination of our method of over-sampling the minority (abnormal) class and under-sampling the majority (normal) class can achieve better classifier performance (in ROC space) than only under-sampling the majority class
- SMOTE — Version 0. 14. 1 - imbalanced-learn
Class to perform over-sampling using SMOTE This object is an implementation of SMOTE - Synthetic Minority Over-sampling Technique as presented in [1] Read more in the User Guide Sampling information to resample the data set
- 什么是 SMOTE（合成少数类过采样技术）？
SMOTE 即合成少数类过采样技术（Synthetic Minority Over - sampling Technique），它是一种在机器学习中处理数据不平衡问题的技术。 当数据集中不同类别的样本数量存在很大差异时，比如一个类别有大量样本，而另一个类别只有少量样本，这种不平衡会影响模型的训练
- Synthetic minority oversampling technique - Wikipedia
In statistics, synthetic minority oversampling technique (SMOTE) is a method for oversampling samples when dealing with imbalanced classification categories within a dataset
- SMOTE for Imbalanced Classification with Python
SMOTE is a data-level resampling technique that generates synthetic (artificial) samples for the minority class Instead of simply duplicating existing examples, it creates new data points by interpolating between existing ones
- A Comprehensive Analysis of Synthetic Minority Oversampling Technique . . .
One approach for handling imbalance is to generate extra data from the minority class, to overcome its shortage of data The Synthetic Minority over-sampling TEchnique (SMOTE) is one of the dominant methods in the literature that achieves this extra sample generation
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