Estimating Confidences for Classifier Decisions using Extreme Value Theory
Classifiers generally lack a mechanism to compute decision confidences. As humans, when we sense that the confidence for a decision is low, we either conduct additional actions to improve our confidence or dismiss the decision. While this reasoning is natural to us, it is currently missing in most common decision algorithms (i.e., classifiers) used in computer vision or machine learning. This limits the capability for a machine to take further actions to either improve a result or dismiss the decision. In this thesis, we design algorithms for estimating the confidence for decisions made by classifiers such as nearest-neighbor or support vector machines. We developed these algorithms leveraging the theory of extreme values. We use the statistical models that this theory provides for modeling the classifier's decision scores for correct and incorrect outcomes. Our proposed algorithms exploit these statistical models in order to compute a correctness belief: the probability that the classifier's decision is correct. In this work, we show how these beliefs can be used to filter bad classifications and to speed up robust estimations via sample and consensus algorithms, which are used in computer vision for estimating camera motions and for reconstructing the scene's 3D structure. Moreover, we show how these beliefs improve the classification accuracy of one-class support vector machines. In conclusion, we show that extreme value theory leads to powerful mechanisms that can predict the correctness of a classifier's decision.