Mining Distributed Evolving Data Streams Using Fractal GP Ensembles

@inproceedings{Folino2007MiningDE,
  title={Mining Distributed Evolving Data Streams Using Fractal GP Ensembles},
  author={Gianluigi Folino and Clara Pizzuti and Giandomenico Spezzano},
  booktitle={EuroGP},
  year={2007}
}
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… CONTINUE READING

Figures, Tables, and Topics from this paper.

Citations

Publications citing this paper.
SHOWING 1-10 OF 10 CITATIONS

An Adaptive Selective Ensemble for Data Streams Classification

VIEW 4 EXCERPTS
CITES BACKGROUND & METHODS
HIGHLY INFLUENCED

A Peer-to-Peer Architecture for Detecting Attacks from Network Traffic and Log Data

  • 2017 International Conference on High Performance Computing & Simulation (HPCS)
  • 2017
VIEW 1 EXCERPT
CITES METHODS

Adapting to concept drift with genetic programming for classifying streaming data

  • 2016 IEEE Congress on Evolutionary Computation (CEC)
  • 2016
VIEW 1 EXCERPT
CITES METHODS

Comparison between Genetic Programming and full model selection on classification problems

  • 2014 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)
  • 2014
VIEW 1 EXCERPT
CITES RESULTS

Stream mining: a novel architecture for ensemble-based classification

  • Knowledge and Information Systems
  • 2011
VIEW 2 EXCERPTS
CITES BACKGROUND

A Survey on the Application of Genetic Programming to Classification

  • IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews)
  • 2010
VIEW 3 EXCERPTS
CITES METHODS

References

Publications referenced by this paper.
SHOWING 1-10 OF 19 REFERENCES

Experiments with a new boosting algorithm

Y. Freund, R. Scapire
  • In Proceedings of the 13th Int. Conference on Machine Learning,
  • 1996
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Dietterich . An experimental comparison of three methods for costructing ensembles of decision trees : Bagging , boosting , and randomization

G. Thomas
  • Machine Learning
  • 2000