OTLP: Output Thresholding Using Mixed Integer Linear Programming
This provides a flexible thresholding solution for real-world applications with imbalanced data, though it appears incremental as it builds on existing thresholding techniques with a new optimization approach.
The paper tackles the problem of finding optimal classification thresholds for imbalanced datasets by proposing OTLP, a model-agnostic framework using mixed integer linear programming that supports various objective functions and constraints. The framework was evaluated on a Credit Card Fraud Detection Dataset, demonstrating its practical utility.
Output thresholding is the technique to search for the best threshold to be used during inference for any classifiers that can produce probability estimates on train and testing datasets. It is particularly useful in high imbalance classification problems where the default threshold is not able to refer to imbalance in class distributions and fail to give the best performance. This paper proposes OTLP, a thresholding framework using mixed integer linear programming which is model agnostic, can support different objective functions and different set of constraints for a diverse set of problems including both balanced and imbalanced classification problems. It is particularly useful in real world applications where the theoretical thresholding techniques are not able to address to product related requirements and complexity of the applications which utilize machine learning models. Through the use of Credit Card Fraud Detection Dataset, we evaluate the usefulness of the framework.