You are required to read and agree to the below before accessing a full-text version of an article in the IDE article repository.

The full-text document you are about to access is subject to national and international copyright laws. In most cases (but not necessarily all) the consequence is that personal use is allowed given that the copyright owner is duly acknowledged and respected. All other use (typically) require an explicit permission (often in writing) by the copyright owner.

For the reports in this repository we specifically note that

  • the use of articles under IEEE copyright is governed by the IEEE copyright policy (available at
  • the use of articles under ACM copyright is governed by the ACM copyright policy (available at
  • technical reports and other articles issued by M‰lardalen University is free for personal use. For other use, the explicit consent of the authors is required
  • in other cases, please contact the copyright owner for detailed information

By accepting I agree to acknowledge and respect the rights of the copyright owner of the document I am about to access.

If you are in doubt, feel free to contact

KNNOR: An oversampling technique for imbalanced datasets


Ashhadul Islam , Samir Belhaouari , Atiq Ur Rehman, Halima Bensmail

Publication Type:

Journal article


Applied Soft Computing


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.


author = {Ashhadul Islam and Samir Belhaouari and Atiq Ur Rehman and Halima Bensmail},
title = {KNNOR: An oversampling technique for imbalanced datasets},
volume = {113},
pages = {1--18},
month = {January},
year = {2022},
journal = {Applied Soft Computing},
url = {}