A Hybrid Mutual-Information and Heatmap Driven Under-Sampling Algorithm for Imbalanced Binary Classification

Class imbalance is a common problem that occurs in classification where one class has much more instances than the other class. Class imbalance is especially challenging in high-stakes fields like medical diagnosis, fraud detection and predictive maintenance among others. In cases of imbalanced class distribution, models perform well while predicting the majority class but are unable to predict the minority class, which is usually more important. This paper introduces the MI-Heat (Mutual-Information and Heatmap Driven Under-sampling), a hybrid algorithm which targets the binary classification problem. The algorithm is a data-level method that combines Mutual Information (MI) for identifying important features and K-Means clustering for identification of the most important majority class samples. In addition, a distance heatmap is used to project proximities among samples and cluster centers, guiding what majority instances to retain and what to discard. Together, Mutual Information, clustering, and heatmap preserve diversity and suppress noise in order to increase the ability of the model to represent both classes equally with clarity. Performance of the MI-Heat algorithm is tested on 23 benchmark datasets and the results are seen as an improvement in classification accuracy, minority class recall and model generalization. When compared to the traditional under-sampling approaches, MI-Heat performance is consistently better, which clearly demonstrates its dominance over dealing with the class imbalance issue.

@article{Gul2025,
  title     = {A Hybrid Mutual-Information and Heatmap Driven Under-Sampling Algorithm for Imbalanced Binary Classification},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {11},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Sehar Gul and Syahid Anuar and Hazlifah Mohd Rusli and Azri Azmi and Sadaquat Ali Ruk},
  doi       = {10.14569/IJACSA.2025.0161197},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161197}
}