Optimizing Accuracy, Recall, Specificity, and Precision Using ILP
Arash Marioriyad, Pouria Ramazi · Mathematics · 2025
Accuracy, recall, specificity, and precision are key performance measures for binary classifiers. To obtain these measures, the probabilities generated by classifiers must be converted into deterministic labels using a threshold. Exhaustive search methods can be computationally expensive, prompting the need for a more efficient solution. We propose an integer linear programming (ILP) formulation to find the threshold that maximizes any linear combination of these measures. Simulations and experiments on four real-world datasets demonstrate that our approach identifies the optimal threshold orders of magnitude faster than an exhaustive search. This work establishes ILP as an efficient tool for optimizing classifier performance.