How to adjust an ensemble size in stream data mining?

@article{Pietruczuk2017HowTA,
  title={How to adjust an ensemble size in stream data mining?},
  author={Lena Pietruczuk and Leszek Rutkowski and Maciej Jaworski and Piotr Duda},
  journal={Inf. Sci.},
  year={2017},
  volume={381},
  pages={46-54}
}
In this paper we propose a new approach for designing an ensemble applied to stream data classification. Our approach is supported by two theorems showing how to decide whether a new component should be added to the ensemble or not, based on the assumption that such an action should increase the accuracy of the ensemble not only for the current portion of observations but also for the whole (infinite) data stream. The conclusions of these theorems hold with a certain probability (confidence… CONTINUE READING

From This Paper

Figures, tables, and topics from this paper.

References

Publications referenced by this paper.
Showing 1-10 of 52 references

UCI Machine Learning Repository

M. Lichman
University of California, Irvine, School of Information and Computer Sciences, http://archive.ics.uci.edu/ml/, 2013 • 2015
View 4 Excerpts
Highly Influenced

B

A. Bifet, G. Holmes, R. Kirkby
Pfahringer, MOA: Massive Online Analysis; Journal of Machine Learning Research 11 • 2010
View 4 Excerpts
Highly Influenced

Reacting to Different Types of Concept Drift: The Accuracy Updated Ensemble Algorithm

IEEE Transactions on Neural Networks and Learning Systems • 2014
View 4 Excerpts
Highly Influenced

Asymptotic statistics, New York

A. W. van der Vaart
1998
View 3 Excerpts
Highly Influenced

A New Method for Data Stream Mining Based on the Misclassification Error

IEEE Transactions on Neural Networks and Learning Systems • 2015
View 3 Excerpts

Similar Papers

Loading similar papers…