Synthetic Minority Over-sampling Technique.

Introduced by Chawla et al. in SMOTE: Synthetic Minority Over-sampling Technique

Perhaps the most widely used approach to synthesizing new examples is called the Synthetic Minority Oversampling Technique, or SMOTE for short. This technique was described by Nitesh Chawla, et al. in their 2002 paper named for the technique titled “SMOTE: Synthetic Minority Over-sampling Technique.”

SMOTE works by selecting examples that are close in the feature space, drawing a line between the examples in the feature space and drawing a new sample at a point along that line.

Source: SMOTE: Synthetic Minority Over-sampling Technique

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Papers

Paper	Code	Results	Date	Stars

Tasks

Task	Papers	Share
General Classification	23	16.55%
Classification	17	12.23%
imbalanced classification	9	6.47%
Fraud Detection	9	6.47%
Intrusion Detection	6	4.32%
Anomaly Detection	5	3.60%
Network Intrusion Detection	4	2.88%
Imputation	3	2.16%
Synthetic Data Generation	3	2.16%

Usage Over Time

This feature is experimental; we are continuously improving our matching algorithm.

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Downsampling