Conformal Prediction for Reliable Machine Learning

Chapter 4

Anomaly Detection

Rikard Laxhammar,    Saab AB, Järfälla, Sweden

Abstract

This chapter presents an extension of conformal prediction for anomaly detection applications. It includes the presentation and discussion of the Conformal Anomaly Detector (CAD) and the computationally more efficient Inductive Conformal Anomaly Detector (ICAD), which are general algorithms for unsupervised or semi-supervised and offline or online anomaly detection. One of the key properties of CAD and ICAD is that the rate of detected anomalies is well calibrated in the online setting under the randomness assumption. Similar to conformal prediction, the choice of Nonconformity Measure (NCM) is of central importance for the classification performance of CAD and ...