Unlabelled data appear in many domains and are particularly relevant to streaming
applications, where even though data is abundant, labelled data is rare. To address the …

Abstract Concept drift detection is a crucial task in data stream evolving environments. Most
of state of the art approaches designed to tackle this problem monitor the loss of predictive …

Abnormal behaviour can be an indicator for a medical condition in older adults. Our novel
unsupervised statistical concept drift detection approach uses variational autoencoders for …

Abstract Concept drift (and data drift) is a common phenomenon in machine learning
models, where the statistical properties of the input data change over time, leading to a …

Recent research yielded a wide array of drift detectors. However, in order to achieve
remarkable performance, the true class labels must be available during the drift detection …

Recent research yielded a wide array of drift detectors. However, in order to achieve
remarkable performance, the true class labels must be available during the drift detection …

Abstract Machine learning models encounter plethora of challenges due to the changing
data over time. This phenomenon is known as concept drift. Existing techniques for concept …

Process mining is an emerging field which focuses on applying data mining techniques over
business process data. Recently, with the improvements in sensoring, collection, and storing …

As more and more data is generated and stored, and as longer data streams become
available, concept drift detection is becoming crucial for most real world applications. We …

The widespread adoption of Artificial Intelligence has led the number of machinelearning
(ML) models created and used in production to exponentially grow. The ability of ML models …