Classifying Evolving Data Streams Using Dynamic Streaming Random Forests
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
Partial drift detection using a rule induction framework
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Mining concept-drifting data streams containing labeled and unlabeled instances
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
Exploiting concept clumping for efficient incremental e-mail categorization
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
Context change detection for resource allocation in service-oriented systems
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part II
Time variance and defect prediction in software projects
Empirical Software Engineering
Label free change detection on streaming data with cooperative multi-objective genetic programming
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
A survey on concept drift adaptation
ACM Computing Surveys (CSUR)
Classifying evolving data streams with partially labeled data
Intelligent Data Analysis
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When monitoring sensory data (e.g., from a wearable device) the context oftentimes changes abruptly: people move from one situation (e.g., working quietly in their office) to another (e.g., being interrupted by one's manager). These context changes can be treated like concept shifts, since the underlying data generator (the concept) changes while moving from one context situation to another. We present an entropy based measure for data streams that is suitable to detect concept shifts in a reliable, noise-resistant, fast, and computationally efficient way. We assess the entropy measure under different concept shift conditions. To support our claims we illustrate the concept shift behavior of the stream entropy. We also present a simple algorithm control approach to show how useful and reliable the information obtained by the entropy measure is compared to a ensemble learner as well as an experimentally inferred upper limit. Our analysis is based on three large synthetic data sets representing real, virtual, and a combination of both concept drifts under different noise conditions (up to 50%). Last but not least, we demonstrate the usefulness of the entropy based measure context switch indication in a real world application in the context-awareness/wearable computing domain.