Modified synthetic minority oversampling technique (MSMOTE) for imbalanced data

Data Science, AI and ML
#1

It is a modified version of SMOTE. SMOTE does not consider the underlying distribution of the minority class and latent noises in the dataset. To improve the performance of SMOTE a modified method MSMOTE is used.

This algorithm classifies the samples of minority classes into 3 distinct groups – Security/Safe samples, Border samples, and latent nose samples. This is done by calculating the distances among samples of the minority class and samples of the training data.

Security samples are those data points which can improve the performance of a classifier. While on the other hand, noise are the data points which can reduce the performance of the classifier. The ones which are difficult to categorize into any of the two are classified as border samples.

While the basic flow of MSOMTE is the same as that of SMOTE (discussed in the previous section). In MSMOTE the strategy of selecting nearest neighbors is different from SMOTE. The algorithm randomly selects a data point from the k nearest neighbors for the security sample, selects the nearest neighbor from the border samples and does nothing for latent noise.