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.[clarification needed]
There are also more complex oversampling techniques, including the creation of artificial data points.
- Oversampling in signal processing, which has no relation.
- 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)
- Rahman,M.M. Davis,D.N. (2010) Addressing the Class Imbalance Problem in Medical Datasets, International Journal of Machine Learning and Computing vol. 3, no. 2, pp. 224–228, 2013.
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