K Nearest Neighbor OveRsampling approach: An open source python package for data augmentation

Abstract

Data is present in abundance, but the problem of imbalanced dataset crops up time and again, vexing classifiers and reducing accuracy. This paper introduces K Nearest Neighbor OveRsampling (KNNOR) Algorithm — a novel data augmentation technique that considers the distribution of data and takes into account the k nearest neighbors while generating artificial data points. The KNNOR algorithm has outperformed the state-of-the-art augmentation algorithms by enabling classifiers to achieve much higher accuracy after injecting artificial minority datapoints into imbalanced datasets. This method is useful especially in health datasets where an imbalance is common and can even be applied to images of lower dimensions.