SMOTE

SMOTE – Synthetic Minority Oversampling Technique

SMOTE (Synthetic Minority Oversampling Technique) is an algorithm used in supervised learning to address class imbalance problems in datasets. Class imbalance occurs when the data set contains more or fewer instances of one class than another. SMOTE works by generating synthetic instances (non-existing) along the direction of the line joining two existing instances, of the minority class data in the data set.

It is a technique utilized to reduce the probability of a classifier predicting a class incorrectly. SMOTE is beneficial in that it allows an artificial minority class up-sampling, which is helpful when the class data is scarce. The technique works by taking a random sample from the minority class (nearby neighbors) and then creating synthetic data points that are added to the minority class with randomized features that are synthesized along the direction of the vector from the minority class to the nearest neighbors in the majority class.

The method has proven to be effective when dealing with problem sets that are either extremely imbalanced or those that have a nonlinear decision boundary. SMOTE is especially useful when there are a lot of samples of the majority class, but not enough samples of the minority class. It can improve the accuracy and reduce the bias of classifiers, as well as reduce the cost of false positive errors.

Due to its effectiveness, SMOTE has become widely used in various fields such as computer vision, in finance and in ecotoxicology. It is also used widely in machine learning and predictive analytics use cases in the fields of computer programming and cybersecurity.