Optimizing Accuracy, Recall, Specificity, and Precision Using ILP

Source abstract

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.