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Found 4 papers, 3 papers with code
Efficient pattern-based anomaly detection in a network of multivariate devices
Moreover, we aim to support end-users in not only in locating entities and sensors causing an anomaly at a certain period, but also explain this decision.
Modelling Users with Item Metadata for Explainable and Interactive Recommendation
The more interpretable the profile and this "matching function" are, the easier it is to provide users with accurate and intuitive explanations, and also to let them interact with the system.
Proximity Forest: An effective and scalable distance-based classifier for time series
We demonstrate on a 1M time series Earth observation dataset that Proximity Forest retains this accuracy on datasets that are many orders of magnitude greater than those in the UCR repository, while learning its models at least 100, 000 times faster than current state of the art models Elastic Ensemble and COTE.
Understanding Concept Drift
Concept drift is a major issue that greatly affects the accuracy and reliability of many real-world applications of machine learning.
