A Mathematical Programming Model with Cost Sensitivity in the Objective Function for Imbalanced Datasets Challenges

Abstract

This paper introduces CS-MSD, a cost-sensitive deviation minimization model designed to address a typical issue of imbalanced datasets in binary classification, which is a major problem in machine learning tasks across various areas. Imbalanced datasets, in which a single class significantly dominates the other, frequently generate biased models that neglect the minority class, making them extremely important in practical sectors like health services and financial services. The Traditional re-sampling techniques, including under-sampling and over-sampling, have associated limitations, such as information loss and over-fitting. CS-MSD overcomes these limitations by combining external deviations with cost sensitivity, which produces a perfect balance of minority and majority class costs. The model outperforms Decision Tree and Radial SVM, achieving a Recall of 0.958 on the win dataset, alongside Specificity and G-mean metrics. With CPU and wall times of 0.052 s and 0.054 s, respectively, CS-MSD also surpasses Random Forest and Bagging in computational efficiency, making it ideal for time-sensitive tasks. Its combined flexibility and processing speed establish CS-MSD as a vital solution for enhancing classification performance across diverse domains.