SMOTE (Synthetic Minority Oversampling Technique) is one of the most commonly used oversampling methods to solve the imbalance problem. 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 synthesize elements for the minority class. The algorithm 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.

Tomek Links is an under-sampling technique that was developed in 1976 by Ivan Tomek. It is one of a modification from Condensed Nearest Neighbors (CNN). It can be used to find desired samples of data from the majority class that is having the lowest Euclidean distance with the minority class data and then remove it.

A combination of over-sampling the minority (abnormal) class and under-sampling the majority (normal) class can achieve better classifier performance than only under-sampling the majority class. This method was first introduced by Batista et al. (2003).

The process of SMOTE-Tomek Links is as follows.

1. Start of SMOTE: choose random data from the minority class.
2. Calculate the distance between the random data and its k nearest neighbors.
3. Multiply the difference with a random number between 0 and 1, then add the result to the minority class as a synthetic sample.
4. Repeat step number 2–3 until the desired proportion of minority class is met (End of SMOTE).
5. Start of Tomek Links: choose random data from the majority class.
6. If the random data’s nearest neighbor is the data from the minority class (i.e. create the Tomek Link), then remove the Tomek Link.