CALDS: context-aware learning from data streams
Proceedings of the First International Workshop on Novel Data Stream Pattern Mining Techniques
Tracking recurrent concepts using context
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Learning recurring concepts from data streams with a context-aware ensemble
Proceedings of the 2011 ACM Symposium on Applied Computing
Identifying hidden contexts in classification
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
Learning about the learning process
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Mining Recurring Concept Drifts with Limited Labeled Streaming Data
ACM Transactions on Intelligent Systems and Technology (TIST)
An instance-window based classification algorithm for handling gradual concept drifts
ADMI'11 Proceedings of the 7th international conference on Agents and Data Mining Interaction
Semi-supervised ensemble learning of data streams in the presence of concept drift
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
Improving tweet stream classification by detecting changes in word probability
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
New management operations on classifiers pool to track recurring concepts
DaWaK'12 Proceedings of the 14th international conference on Data Warehousing and Knowledge Discovery
On evaluating stream learning algorithms
Machine Learning
On-line bayesian context change detection in web service systems
Proceedings of the 2013 international workshop on Hot topics in cloud services
RCD: A recurring concept drift framework
Pattern Recognition Letters
Stream-based event prediction using bayesian and bloom filters
Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering
A survey on concept drift adaptation
ACM Computing Surveys (CSUR)
Just-in-time adaptive similarity component analysis in nonstationary environments
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Tracking recurrent concepts using context
Intelligent Data Analysis - Combined Learning Methods and Mining Complex Data
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Concept drift constitutes a challenging problem for the machine learning and data mining community that frequently appears in real world stream classification problems. It is usually defined as the unforeseeable concept change of the target variable in a prediction task. In this paper, we focus on the problem of recurring contexts, a special sub-type of concept drift, that has not yet met the proper attention from the research community. In the case of recurring contexts, concepts may re-appear in future and thus older classification models might be beneficial for future classifications. We propose a general framework for classifying data streams by exploiting stream clustering in order to dynamically build and update an ensemble of incremental classifiers. To achieve this, a transformation function that maps batches of examples into a new conceptual representation model is proposed. The clustering algorithm is then applied in order to group batches of examples into concepts and identify recurring contexts. The ensemble is produced by creating and maintaining an incremental classifier for every concept discovered in the data stream. An experimental study is performed using (a) two new real-world concept drifting datasets from the email domain, (b) an instantiation of the proposed framework and (c) five methods for dealing with drifting concepts. Results indicate the effectiveness of the proposed representation and the suitability of the concept-specific classifiers for problems with recurring contexts.