Real-time ranking with concept drift using expert advice
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Conceptual equivalence for contrast mining in classification learning
Data & Knowledge Engineering
Class Specific Fuzzy Decision Trees for Mining High Speed Data Streams
Fundamenta Informaticae
Data Mining and Knowledge Discovery
CALDS: context-aware learning from data streams
Proceedings of the First International Workshop on Novel Data Stream Pattern Mining Techniques
On classifying drifting concepts in P2P networks
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Learning recurring concepts from data streams with a context-aware ensemble
Proceedings of the 2011 ACM Symposium on Applied Computing
Learning about the learning process
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Predicting concept changes using a committee of experts
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
Class Specific Fuzzy Decision Trees for Mining High Speed Data Streams
Fundamenta Informaticae
A survey on concept drift adaptation
ACM Computing Surveys (CSUR)
Tracking recurrent concepts using context
Intelligent Data Analysis - Combined Learning Methods and Mining Complex Data
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Prediction in streaming data is an important activity in the modern society. Two major challenges posed by data streams are (1) the data may grow without limit so that it is difficult to retain a long history of raw data; and (2) the underlying concept of the data may change over time. The novelties of this paper are in four folds. First, it uses a measure of conceptual equivalence to organize the data history into a history of concepts. This contrasts to the common practice that only keeps recent raw data. The concept history is compact while still retains essential information for learning. Second, it learns concept-transition patterns from the concept history and anticipates what the concept will be in the case of a concept change. It then proactively prepares a prediction model for the future change. This contrasts to the conventional methodology that passively waits until the change happens. Third, it incorporates proactive and reactive predictions. If the anticipation turns out to be correct, a proper prediction model can be launched instantly upon the concept change. If not, it promptly resorts to a reactive mode: adapting a prediction model to the new data. Finally, an efficient and effective system RePro is proposed to implement these new ideas. It carries out prediction at two levels, a general level of predicting each oncoming concept and a specific level of predicting each instance's class. Experiments are conducted to compare RePro with representative existing prediction methods on various benchmark data sets that represent diversified scenarios of concept change. Empirical evidence offers inspiring insights and demonstrates the proposed methodology is an advisable solution to prediction in data streams.