Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
A streaming ensemble algorithm (SEA) for large-scale classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Incremental Learning from Noisy Data
Machine Learning
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Combining proactive and reactive predictions for data streams
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Decision trees for mining data streams
Intelligent Data Analysis
Tracking Recurrent Concept Drift in Streaming Data Using Ensemble Classifiers
ICMLA '07 Proceedings of the Sixth International Conference on Machine Learning and Applications
Learning from Data Streams: Processing Techniques in Sensor Networks
Learning from Data Streams: Processing Techniques in Sensor Networks
Knowledge Discovery from Sensor Data
Knowledge Discovery from Sensor Data
An Ensemble of Classifiers for coping with Recurring Contexts in Data Streams
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Tracking recurrent concepts using context
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Improving the learning of recurring concepts through high-level fuzzy contexts
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
Learning recurring concepts from data streams with a context-aware ensemble
Proceedings of the 2011 ACM Symposium on Applied Computing
Artificial recurrence for classification of streaming data with concept shift
ICAIS'11 Proceedings of the Second international conference on Adaptive and intelligent systems
Data with shifting concept classification using simulated recurrence
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part I
New management operations on classifiers pool to track recurring concepts
DaWaK'12 Proceedings of the 14th international conference on Data Warehousing and Knowledge Discovery
Tracking recurrent concepts using context
Intelligent Data Analysis - Combined Learning Methods and Mining Complex Data
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This work address data stream mining from dynamic environments where the distribution underlying the observations may change over time. In these contexts, learning algorithms must be equipped with change detection mechanisms. Several methods have been proposed able to detect and react to concept drift. When a drift is signaled, most of the approaches use a forgetting mechanism, by releasing the current model, and start learning a new decision model, Nevertheless, it is not rare for the concepts from history to reappear, for example seasonal changes. In this work we present method that memorizes learnt decision models whenever a concept drift is signaled. The system uses meta-learning techniques that characterize the domain of applicability of previous learnt models. The meta-learner can detect re-occurrence of contexts and take pro-active actions by activating previous learnt models. The main benefit of this approach is that the proposed meta-learner is capable of selecting similar historical concept, if there is one, without the knowledge of true classes of examples.