Hands-On Guide To LoRAS: A Better Oversampling Algorithm

Imbalanced datasets are encountered in many fields, where machine learning has found its applications, including business, finance, and biomedical science. In imbalanced datasets, the number of instances in one (or more) class(es) is very low compared to the others. Training standard machine learning models on such datasets leads to the creation of biased models with higher false-positive and true-negative rates.  A common approach for overcoming this issue is generating synthetic instances of the minority class using an oversampling algorithm. SMOTE is a widely used oversampling technique. It selects an arbitrary minority class data point and its k nearest neighbours of the minority class. SMOTE then generates synthetic minority class data points along line segments joining these k ne