# Paired Learners for Concept Drift - PowerPoint PPT Presentation

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## Paired Learners for Concept Drift

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### Dynamic weighted majority: An ensemble method for drifting concepts. Accuracy Weighted Ensemble ... Mining concept drifting data streams using ensemble ... – PowerPoint PPT presentation

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Title: Paired Learners for Concept Drift

1
Paired Learners for Concept Drift
• Stephen H. Bach
• and Marcus A. Maloof
• ICDM 2008

2
Outline
• Introduction
• Algorithm Paired Learner
• Related work
• Experimental study
• Analysis and discussion
• Conclusion

3
Introduction
• Concept may drift
• Stable learner
• predicts based on all of its experience.
• Reactive learner
• predicts based on its experience over a short,
recent window of time

4
Algorithm Paired Learner
5
Algorithm Paired Learner (cont.)
6
Related work
• Streaming Ensemble Algorithm
• W. N. Street and Y. Kim.
• A streaming ensemble algorithm (SEA) for
large-scale classification.
• Dynamic Weighted Majority
• J. Z. Kolter and M. A. Maloof.
• Dynamic weighted majority An ensemble method for
drifting concepts.
• Accuracy Weighted Ensemble
• H. Wang, W. Fan, P. S. Yu, and J. Han.
• Mining concept drifting data streams using
ensemble classifiers.

7
Experimental study
• Base Learner Naïve-Bayes (NB)
• Instance
• distributions for each class Ci
• prior probabilities P(Ci)
• conditional probabilities P( xj Ci)
• most probable class, C

8
Experimental study
9
Experimental study
10
Experimental study
11
Experimental study
12
Analysis and discussion
• Moment of drifts
• If the paired learner replaces S near these
times,
• then it should classify subsequent examples more
accurately.
• f(n) training time, g(n) classifying time
• The time to train a paired learner on n examples
is
• O(nf(w)n g(1)), where w is the size of Rws
window.
• If the base learner retracts examplesO(n f(1)
n g(1)).

13
Analysis and discussion (cont.)
• Economy
• Performed comparably but with less learners than
other algorithms
• Robustness to noise
• Equal to other algorithms

14
Conclusion
• Paired Learner
• We introduced the notion of a paired learner,
which uses the difference in performance between
a reactive learner and a stable learner to cope
with concept drift.
• The properties of reactive learners when they as
indicators