Local-adapted minority oversampling strategy for highly imbalanced highly noisy dataset

Abstract

Applying a local-adapted minority oversampling strategy technique to an imbalanced dataset including positive samples belonging to a minority class and negative samples belonging to a majority class, the minority class being less prevalent than the majority class, wherein each sample from the dataset includes a plurality of features. The local-adapted minority oversampling strategy includes determining a local imbalance for each positive sample from the minority class corresponding to a number of other positive samples and/or negative samples within a neighborhood of each positive sample. The local-adapted minority oversampling strategy also includes calculating a local-adapted oversampling ratio based on the local imbalance estimated for each positive sample and replicating each positive sample using the local-adapted oversampling ratio to generate a new dataset.