PyDRO:

A Python reimplementation of the Distributional Random Oversampling method for binary text classification

This repo is a stand-alone (re)implementation of the Distributional Random Oversampling (DRO) method presented in SIGIR'16. The former implementation was part of the JaTeCs framework for Java.

Distributional Random Oversampling (DRO) is an oversampling method to counter data imbalance in binary text classification. DRO generates new random minority-class synthetic documents by exploiting the distributional properties of the terms in the collection. The variability introduced by the oversampling method is enclosed in a latent space; the original space is replicated and left untouched.

It comes with a main file showing an example of how to use it on Reuters-21578.

Reference:

@inproceedings{moreo2016distributional,
  title={Distributional Random Oversampling for Imbalanced Text Classification},
  author={Moreo, Alejandro and Esuli, Andrea and Sebastiani, Fabrizio},
  booktitle={SIGIR 2016, 39th ACM Conference on Research and Development in Information Retrieval, Pisa, IT},
  year={2016}
}