Tackling Class Imbalance with Ranking

The dataset comes originally from UCI Machine Learning. The multiclass datasets were transformed in binary classification as mentioned in the paper. Ranking methods were applied to improve class imbalance. The datasets are divided in 30 folds so that other class imbalance methods can be compared to the methods in the paper. The code used in the paper is also provided.

Data and Resources

Additional Info

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Source http://archive.ics.uci.edu/ml/
Author Ricardo Cruz; Kelwin Fernandes; Jaime S. Cardoso; Joaquim F. Pinto Costa
Last Updated March 15, 2019, 11:03 (Europe/Lisbon)
Created February 20, 2017, 15:15 (Europe/Lisbon)
DOI https://doi.org/10.25747/R02P-AP28
dc.Date July 2016
dc.Format *.pdf; *.bz2
dc.Format.Extent 14MB
dc.Language En
dc.Publisher INESC TEC
dc.Relation Cruz, R., Fernandes, K., Cardoso, J. S., & Costa, J. F. P. (2016, July). Tackling class imbalance with ranking. In Neural Networks (IJCNN), 2016 International Joint Conference on (pp. 2182-2187). IEEE. DOI: 10.1109/IJCNN.2016.7727469 ; http://ieeexplore.ieee.org/document/7727469/