SMOTE and Edited Nearest Neighbors Undersampling for Imbalanced Datasets

Imbalanced datasets are a special case for classification problem where the class distribution is not uniform among the classes. One of the techniques to handle imbalance datasets is data sampling. Synthetic Minority Oversampling Technique (SMOTE) is an oversampling technique that generates synthetic samples from the minority class to match the majority class. It is used to obtain a synthetically class-balanced or nearly class-balanced training set. 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.