A framework for generating data to simulate changing environments
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Dynamic integration of classifiers for handling concept drift
Information Fusion
Mining Multi-label Concept-Drifting Data Streams Using Dynamic Classifier Ensemble
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Mining multi-label concept-drifting data streams using ensemble classifiers
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 5
Incremental learning with multi-level adaptation
Neurocomputing
Editorial: Classifying text streams by keywords using classifier ensemble
Data & Knowledge Engineering
Handling concept drift in process mining
CAiSE'11 Proceedings of the 23rd international conference on Advanced information systems engineering
A survey on concept drift adaptation
ACM Computing Surveys (CSUR)
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In the real world concepts and data distributions are often not stable but change with time. This problem, known as concept drift, complicates the task of learning a model from data and requires special approaches, different from commonly used techniques, which treat arriving instances as equally important contributors to the target concept. Among the most popular and effective approaches to handle concept drift is ensemble learning, where a set of models built over different time periods is maintained and the best model is selected or the predictions of models are combined. In this paper we consider the use of an ensemble integration technique that helps to better handle concept drift at the instance level. Our experiments with real-world antibiotic resistance data demonstrate that dynamic integration of classifiers built over small time intervals can be more effective than globally weighted voting which is currently the most commonly used integration approach for handling concept drift with ensembles.