Learning in the presence of concept drift and hidden contexts
Gerhard Widmer,Miroslav Kubat +1 more
TLDR
A family of learning algorithms that flexibly react to concept drift and can take advantage of situations where contexts reappear are described, including a heuristic that constantly monitors the system's behavior.Citations
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Book ChapterDOI
A Comparison of Batch and Incremental Supervised Learning Algorithms
TL;DR: It is claimed that incremental learning might be more suitable for learning from frequently updated databases than batch learning, although a number of issues remain to be resolved.
Book ChapterDOI
Towards Hierarchical Classification of Data Streams
TL;DR: This paper proposes an incremental method for hierarchical classification of data streams and experimentally shows that this stream hierarchical classifier present advantages to the traditional online setting in three real-world problems related to entomology, ichthyology, and audio processing.
Proceedings Article
Matchmaker: Data Drift Mitigation in Machine Learning for Large-Scale Systems
TL;DR: The proposed Matchmaker is the first scalable, adaptive, and adaptive solution to the data drift problem in large-scale production systems and introduces a novel similarity metric to address multiple types of data drifts while only incurring limited overhead.
Journal Article
Experiments with two approaches for tracking drifting concepts
TL;DR: This paper addresses the task of learning classiers from streams of labelled data and studies two mechanisms based on the time window idea, which can be used with any learning algorithm to deal with changing concepts.
Forecasting in Database Systems
TL;DR: A novel approach is introduced that seamlessly integrates time series forecasting into an existing database management system and supports a new query type — a forecast query — that enables forecasting of time series data for any user and is automatically processed by the core engine of an existing DBMS.
References
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