KNNOR: An oversampling technique for imbalanced datasets

Predictive performance of Machine Learning (ML) models rely on the quality of data used for training the models. However, if the training data is not balanced among different classes, the performance of ML models deteriorate heavily. Several techniques have been proposed in the literature to add some semblance of balance to the data sets by adding artificial data points. Synthetic Minority Oversampling Technique(SMOTE) and Adaptive Synthetic Sampling(ADASYN) are some of the commonly used techniques to deal with class imbalance. However, these approaches are prone to 'within class imbalance' and 'small disjunct problem'. To overcome these problems, this article proposes an advanced algorithm by studying the compactness and location of the minority class relative to other classes. The proposed technique called K-Nearest Neighbor OveRsampling approach (KNNOR) performs a three step process to identify the critical and safe areas for augmentation and generate synthetic data points of the minority class. The relative density of the entire population is considered while generating artificial points. This enables the proposed KNNOR approach to oversample the minority class more reliably and at the same time stay resilient against noise. The proposed method is compared with the ten top performing contemporary oversamplers by testing the accuracy of classifiers trained on augmented data provided by each oversampler. The experimental results on several common imbalanced datasets show that our method ranks first more consistently than the other state-of-art oversamplers. The proposed method is easy to use and has been made open source as a python library. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).