The Penalized Inverse Probability Measure for Conformal Classification
This work addresses the need for more reliable and efficient conformal prediction methods in real-world applications like agricultural robotics, though it is incremental as it builds on existing nonconformity score frameworks.
The paper tackles the problem of improving the efficiency and informativeness of conformal classifiers by introducing the Penalized Inverse Probability (PIP) nonconformity score and its regularized version RePIP, which jointly optimize these metrics. Empirical results on crop and weed image classification show that PIP-based classifiers achieve a good balance between informativeness and efficiency compared to other measures.
The deployment of safe and trustworthy machine learning systems, and particularly complex black box neural networks, in real-world applications requires reliable and certified guarantees on their performance. The conformal prediction framework offers such formal guarantees by transforming any point into a set predictor with valid, finite-set, guarantees on the coverage of the true at a chosen level of confidence. Central to this methodology is the notion of the nonconformity score function that assigns to each example a measure of ''strangeness'' in comparison with the previously seen observations. While the coverage guarantees are maintained regardless of the nonconformity measure, the point predictor and the dataset, previous research has shown that the performance of a conformal model, as measured by its efficiency (the average size of the predicted sets) and its informativeness (the proportion of prediction sets that are singletons), is influenced by the choice of the nonconformity score function. The current work introduces the Penalized Inverse Probability (PIP) nonconformity score, and its regularized version RePIP, that allow the joint optimization of both efficiency and informativeness. Through toy examples and empirical results on the task of crop and weed image classification in agricultural robotics, the current work shows how PIP-based conformal classifiers exhibit precisely the desired behavior in comparison with other nonconformity measures and strike a good balance between informativeness and efficiency.