Conformal Prediction for Reliable Machine Learning Theory, Adaptations and Applications

The conformal predictions framework is a recent development in machine learning that can associate a reliable measure of confidence with a prediction in any real-world pattern recognition application, including risk-sensitive applications such as medical diagnosis, face recognition, and financial risk prediction. Conformal Predictions for Reliable Machine Learning: Theory, Adaptations and Applications captures the basic theory of the framework, demonstrates how to apply it to real-world problems, and presents several adaptations, including active learning, change detection, and anomaly detection. As practitioners and researchers around the world apply and adapt the framework, this edited volume brings together these bodies of work, providing a springboard for further research as well as a handbook for application in real-world problems.

• Understand the theoretical foundations of this important framework that can provide a reliable measure of confidence with predictions in machine learning
• Be able to apply this framework to real-world problems in different machine learning settings, including classification, regression, and clustering
• Learn effective ways of adapting the framework to newer problem settings, such as active learning, model selection, or change detection

Section I: Theory 1: The Basic Conformal Prediction Framework 2: Beyond the Basic Conformal Prediction Framework

Section II: Adaptations 3: Active Learning using Conformal Prediction 4: Anomaly Detection 5: Online Change Detection by Testing Exchangeability 6. Feature Selection and Conformal Predictors 7. Model Selection 8. Quality Assessment 9. Other Adaptations

Section III: Applications 10. Biometrics 11. Diagnostics and Prognostics by Conformal Predictors 12. Biomedical Applications using Conformal Predictors 13. Reliable Network Traffic Classification and Demand Prediction 14. Other Applications