Machine Learning
BOAT—optimistic decision tree construction
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
A streaming ensemble algorithm (SEA) for large-scale classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Incremental Induction of Decision Trees
Machine Learning
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Inducing oblique decision trees with evolutionary algorithms
IEEE Transactions on Evolutionary Computation
GP ensembles for large-scale data classification
IEEE Transactions on Evolutionary Computation
A survey on the application of genetic programming to classification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Handling different categories of concept drifts in data streams using distributed GP
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
Learning regression ensembles with genetic programming at scale
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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A Genetic Programming based boosting ensemble method for the classification of distributed streaming data is proposed. The approach handles flows of data coming from multiple locations by building a global model obtained by the aggregation of the local models coming from each node. A main characteristics of the algorithm presented is its adaptability in presence of concept drift. Changes in data can cause serious deterioration of the ensemble performance. Our approach is able to discover changes by adopting a strategy based on self-similarity of the ensemble behavior, measured by its fractal dimension, and to revise itself by promptly restoring classification accuracy. Experimental results on a synthetic data set show the validity of the approach in maintaining an accurate and up-to-date GP ensemble.