aiah / rusboost

Classifier of imbalanced data by boosting with random under sampling

RUSBoost

RUSBoost[1] is one of the most famous classification methods for imbalanced data. I implemented this code as is in the publication[1]. However, some ideas to improve a classification result have been proposed. For example, weak classifiers whose error rates are over 0.5 should be removed. I have not implemented these ideas yet.

I welcome your feedback to improve this code.

[1] Seiffert, C.; Khoshgoftaar, T.M.; Van Hulse, J.; Napolitano, A., "RUSBoost: A Hybrid Approach to Alleviating Class Imbalance,", IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, vol.40, no.1, pp.185,197, Jan. 2010

Note

This code can run with Python 2.6. For Python 3.6, this code needs some revisions.