A Priori Reliability Prediction with Meta-Learning Based on Context Information

Machine learning systems are used in a wide variability of tasks, where reliability is very important. Often from the output of these systems their reliability cannot directly be deduced. We propose an approach to predict the reliability of a machine learning system externally. We tackle this by using an additional machine learning component we call meta-learner. This meta-learner can use the original input as well as supplementary context information for its judgment. With this approach the meta-learner can make a prediction of the performance of the machine learner before this one is actually executed. Based on this prediction unreliable decisions can be rejected and the systems reliability is retained. We show that our method outperforms certainty-based approaches at the example of road terrain detection.