A Tutorial on Conformal Prediction
The Journal of Machine Learning Research
Cancer informatics by prototype networks in mass spectrometry
Artificial Intelligence in Medicine
Hedged predictions for traditional Chinese chronic gastritis diagnosis with confidence machine
Computers in Biology and Medicine
Normalized nonconformity measures for regression Conformal Prediction
AIA '08 Proceedings of the 26th IASTED International Conference on Artificial Intelligence and Applications
Conformal prediction for distribution-independent anomaly detection in streaming vessel data
Proceedings of the First International Workshop on Novel Data Stream Pattern Mining Techniques
Learning model trees from evolving data streams
Data Mining and Knowledge Discovery
Regression conformal prediction with nearest neighbours
Journal of Artificial Intelligence Research
Robust re-identification using randomness and statistical learning: Quo vadis
Pattern Recognition Letters
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Recent advances in machine learning make it possible to design efficient prediction algorithms for data sets with huge numbers of parameters. This article describes a new technique for 'hedging' the predictions output by many such algorithms, including support vector machines, kernel ridge regression, kernel nearest neighbours and by many other state-of-the-art methods. The hedged predictions for the labels of new objects include quantitative measures of their own accuracy and reliability. These measures are provably valid under the assumption of randomness, traditional in machine learning: the objects and their labels are assumed to be generated independently from the same probability distribution. In particular, it becomes possible to control (up to statistical fluctuations) the number of erroneous predictions by selecting a suitable confidence level. Validity being achieved automatically, the remaining goal of hedged prediction is efficiency: taking full account of the new objects' features and other available information to produce as accurate predictions as possible. This can be done successfully using the powerful machinery of modern machine learning.