Fingerprinting Concepts in Data Streams with Supervised and Unsupervised Meta-Information

• URL: http://arxiv.org/abs/2603.11094v1
• Date: Wed, 11 Mar 2026 07:42:11 GMT
• Title: Fingerprinting Concepts in Data Streams with Supervised and Unsupervised Meta-Information
• Abstract summary: Concept drift is a change in the distribution of data over time due to changes in environmental conditions.<n>We propose a framework to represent both supervised and unsupervised behaviours of a concept in a fingerprint.<n>FiCSUM outperforms state-of-the-art methods over a range of 11 real world and synthetic datasets.
• Score: 31.52969462508665
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Streaming sources of data are becoming more common as the ability to collect data in real-time grows. A major concern in dealing with data streams is concept drift, a change in the distribution of data over time, for example, due to changes in environmental conditions. Representing concepts (stationary periods featuring similar behaviour) is a key idea in adapting to concept drift. By testing the similarity of a concept representation to a window of observations, we can detect concept drift to a new or previously seen recurring concept. Concept representations are constructed using meta-information features, values describing aspects of concept behaviour. We find that previously proposed concept representations rely on small numbers of meta-information features. These representations often cannot distinguish concepts, leaving systems vulnerable to concept drift. We propose FiCSUM, a general framework to represent both supervised and unsupervised behaviours of a concept in a fingerprint, a vector of many distinct meta-information features able to uniquely identify more concepts. Our dynamic weighting strategy learns which meta-information features describe concept drift in a given dataset, allowing a diverse set of meta-information features to be used at once. FiCSUM outperforms state-of-the-art methods over a range of 11 real world and synthetic datasets in both accuracy and modeling underlying concept drift.

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