Dealing-With-Imbalanced-Data

Using Imblearn To Tackle Imbalanced Data Sets

Imbalanced data is a frequently occuring feature of data sets found in various fields such as epidemiology, marketing and fraud detection. Here I show examples of some methods for dealing with such data. The data used came from the KEEL data set repository. I used a data set called 'yeast3' which had a class imbalance ratio of 1:8.1.

Resources used:

1. Imbalanced-learn documentation.

http://contrib.scikit-learn.org/imbalanced-learn/index.html

2. Data mining with imbalanced class distributions concepts and methods (Prati et al 2009).

http://conteudo.icmc.usp.br/pessoas/gbatista/files/iicai2009.pdf

3. Resampling techniques and other strategies - Ajinkya More.
   https://www.youtube.com/watch?v=-Z1PaqYKC1w

4. KEEL data set repository.

http://sci2s.ugr.es/keel/imbalanced.php