3.1 Datasets

• imbalanced-learn datasets

  This collection of datasets is from imblearn.datasets.fetch_datasets.

  ID	Name	Repository & Target	Ratio	#S	#F
  1	ecoli	UCI, target: imU	8.6:1	336	7
  2	optical_digits	UCI, target: 8	9.1:1	5,620	64
  3	satimage	UCI, target: 4	9.3:1	6,435	36
  4	pen_digits	UCI, target: 5	9.4:1	10,992	16
  5	abalone	UCI, target: 7	9.7:1	4,177	10
  6	sick_euthyroid	UCI, target: sick euthyroid	9.8:1	3,163	42
  7	spectrometer	UCI, target: > =44	11:1	531	93
  8	car_eval_34	UCI, target: good, v good	12:1	1,728	21
  9	isolet	UCI, target: A, B	12:1	7,797	617
  10	us_crime	UCI, target: >0.65	12:1	1,994	100
  11	yeast_ml8	LIBSVM, target: 8	13:1	2,417	103
  12	scene	LIBSVM, target: >one label	13:1	2,407	294
  13	libras_move	UCI, target: 1	14:1	360	90
  14	thyroid_sick	UCI, target: sick	15:1	3,772	52
  15	coil_2000	KDD, CoIL, target: minority	16:1	9,822	85
  16	arrhythmia	UCI, target: 06	17:1	452	278
  17	solar_flare_m0	UCI, target: M->0	19:1	1,389	32
  18	oil	UCI, target: minority	22:1	937	49
  19	car_eval_4	UCI, target: vgood	26:1	1,728	21
  20	wine_quality	UCI, wine, target: <=4	26:1	4,898	11
  21	letter_img	UCI, target: Z	26:1	20,000	16
  22	yeast_me2	UCI, target: ME2	28:1	1,484	8
  23	webpage	LIBSVM, w7a, target: minority	33:1	34,780	300
  24	ozone_level	UCI, ozone, data	34:1	2,536	72
  25	mammography	UCI, target: minority	42:1	11,183	6
  26	protein_homo	KDD CUP 2004, minority	111:1	145,751	74
  27	abalone_19	UCI, target: 19	130:1	4,177	10

• Imbalanced Databases

  Link: https://github.com/gykovacs/mldb

3.2 Github Repositories

3.2.1 Algorithms & Utilities & Jupyter Notebooks

• imbalanced-algorithms - Python-based implementations of algorithms for learning on imbalanced data.
• imbalanced-dataset-sampler - A (PyTorch) imbalanced dataset sampler for oversampling low frequent classes and undersampling high frequent ones.
• class_imbalance - Jupyter Notebook presentation for class imbalance in binary classification.
• Multi-class-with-imbalanced-dataset-classification - Perform multi-class classification on imbalanced 20-news-group dataset.
• Advanced Machine Learning with scikit-learn: Imbalanced classification and text data - Different approaches to feature selection, and resampling methods for imbalanced data.

3.2.2 Paper list

• Anomaly Detection Learning Resources by yzhao062 - Anomaly detection related books, papers, videos, and toolboxes.
• Paper-list-on-Imbalanced-Time-series-Classification-with-Deep-Learning - Imbalanced Time-series Classification

3.2.3 Slides

• acm_imbalanced_learning - slides and code for the ACM Imbalanced Learning talk on 27th April 2016 in Austin, TX.

Contributors ✨

Thanks goes to these wonderful people (emoji key):

Zhining Liu 💻 🚧 🌍

曾阿信 🚧

WonJun Moon 💻

Gang Liu 💻

This project follows the all-contributors specification. Contributions of any kind welcome!