---
# System prepended metadata

title: Imbalanced data
tags: [ikmlab]

---

# Imbalanced data

###### tags: `ikmlab`
---

## 投影片

[連結](https://docs.google.com/presentation/d/1vKd4RPvCwWzMyQODETAvaInIB70jBIVJyfd5kzMBVgk/edit#slide=id.p)

## 參考資源

[5 Techniques to work with Imbalanced Data in Machine Learning](https://towardsdatascience.com/5-techniques-to-work-with-imbalanced-data-in-machine-learning-80836d45d30c?fbclid=IwAR3WLL9eY2qBJm6MiT_184wTkkMg7AzdijevvvkMZd7AjtkUmMagkGW7c1o)

[SMOTE + ENN : 解決數據不平衡建模的採樣方法](https://medium.com/%E6%95%B8%E5%AD%B8-%E4%BA%BA%E5%B7%A5%E6%99%BA%E6%85%A7%E8%88%87%E8%9F%92%E8%9B%87/smote-enn-%E8%A7%A3%E6%B1%BA%E6%95%B8%E6%93%9A%E4%B8%8D%E5%B9%B3%E8%A1%A1%E5%BB%BA%E6%A8%A1%E7%9A%84%E6%8E%A1%E6%A8%A3%E6%96%B9%E6%B3%95-cdb6324b711e?fbclid=IwAR1xEsLjtrHi2PSmkP-uqPEbgWy18aiUc_BV_cS5pWTgiAkdllnXTPDtw-Y)

[paper](https://www.ele.uri.edu/faculty/he/PDFfiles/ImbalancedLearning.pdf?fbclid=IwAR3tJe1MIEIC3jY3-xcczzLS4gULtCn4oKbi5rcJqcMZHLuX-JQlH29ylDU)

[kaggle example](https://www.kaggle.com/rafjaa/resampling-strategies-for-imbalanced-datasets?fbclid=IwAR271yoaSePCjNons5qWmyFcYWsL8Ecp6pqyhqYMs5njP1CgH-KS5nVGgZU#t6)

[In classification, how do you handle an unbalanced training set?](https://www.quora.com/In-classification-how-do-you-handle-an-unbalanced-training-set?fbclid=IwAR3xAyr4wFp_AcZsbVaEMZQynrGpqm9h4BZ1RipqOfbPeeQJ04FXBhpEgYE)

[imlearn SMOTE](https://imbalanced-learn.org/stable/over_sampling.html?fbclid=IwAR1QXfwhh0q-Q1RBDs7H-joyTiaV-2_0OlJoDL9oYbxKyYGfONvCnEKj04c#smote-variants)

[Resampling to Properly Handle Imbalanced Datasets in Machine Learning](https://hackmd.io/VA23yKVuTcqnrshJMwblkw?both)

[機器學習之類別不平衡問題：從資料集角度處理不平衡問題（二）](https://www.gushiciku.cn/pl/2LNL/zh-tw)

[SMOTE paper](https://arxiv.org/pdf/1106.1813.pdf)