Oversampling and undersampling in data analysis

Oversampling and undersampling in data analysis are techniques used to adjust the class distribution of a data set (i.e. the ratio between the different classes/categories represented).

Oversampling and undersampling are opposite and roughly equivalent techniques. They both involve using a bias to select more samples from one class than from another.

The usual reason for oversampling is to correct for a bias in the original dataset. One scenario where it is useful is when training a classifier using labelled training data from a biased source, since labelled training data is valuable but often comes from un-representative sources.

For example, suppose we have a sample of 1000 people of which 66% are male (perhaps the sample was collected at a football match). We know the general population is 50% female, and we may wish to adjust our dataset to represent this. Simple oversampling will select each female example twice, and this copying will produce a balanced dataset of 1333 samples with 50% female. Simple undersampling will drop some of the male samples at random to give a balanced dataset of 667 samples, again with 50% female.

There are also more complex oversampling techniques, including the creation of artificial data points.

See also

• Oversampling in signal processing, which is no relation.

References

• Chawla, Nitesh V. (2010) Data Mining for Imbalanced Datasets: An Overview doi:10.1007/978-0-387-09823-4_45 In: Maimon, Oded; Rokach, Lior (Eds) Data Mining and Knowledge Discovery Handbook, Springer ISBN 978-0-387-09823-4 (pages 875-886)