yejg2017 / imbalanced-algorithms

Python-based implementations of algorithms for learning on imbalanced data.

ND DIAL: Imbalanced Algorithms

Minimalist Python-based implementations of algorithms for imbalanced learning. Includes deep and representational learning algorithms (implemented via TensorFlow). Below is a list of the methods currently implemented.

• Undersampling

  1. Random Majority Undersampling with/without Replacement

• Oversampling

  1. SMOTE - Synthetic Minority Over-sampling Technique¹
  2. DAE - Denoising Autoencoder² (TensorFlow)
  3. GAN - Generative Adversarial Network³ (TensorFlow)
  4. VAE - Variational Autoencoder⁴ (TensorFlow)

• Ensemble Sampling

  1. RAMOBoost⁵
  2. RUSBoost⁶
  3. SMOTEBoost⁷

References:

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1. N. V. Chawla, K. W. Bowyer, L. O. Hall, and P. Kegelmeyer. "SMOTE

   Synthetic Minority Over-Sampling Technique." Journal of Artificial Intelligence Research (JAIR), 2002.

   ↩

2. P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P.-A. Manzagol. "Stacked Denoising Autoencoders

   Learning Useful Representations in a Deep Network with a Local Denoising Criterion". Journal of Machine Learning Research (JMLR), 2010.

   ↩

3. : I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. "Generative Adversarial Nets". Advances in Neural Information Processing Systems 27 (NIPS), 2014.↩

4. : D. P. Kingma and M. Welling. "Auto-Encoding Variational Bayes". arXiv preprint arXiv:1312.6114, 2013.↩

5. S. Chen, H. He, and E. A. Garcia. "RAMOBoost

   Ranked Minority Oversampling in Boosting". IEEE Transactions on Neural Networks, 2010.

   ↩

6. C. Seiffert, T. M. Khoshgoftaar, J. V. Hulse, and A. Napolitano. "RUSBoost

   Improving Classification Performance when Training Data is Skewed". International Conference on Pattern Recognition (ICPR), 2008.

   ↩

7. N. V. Chawla, A. Lazarevic, L. O. Hall, and K. W. Bowyer. "SMOTEBoost

   Improving Prediction of the Minority Class in Boosting." European Conference on Principles of Data Mining and Knowledge Discovery (PKDD), 2003.

   ↩