Knowledge and Information Systems

, Volume 39, Issue 3, pp 565–578 | Cite as

Controlled permutations for testing adaptive learning models

Regular Paper

Abstract

We study evaluation of supervised learning models that adapt to changing data distribution over time (concept drift). The standard testing procedure that simulates online arrival of data (test-then-train) may not be sufficient to generalize about the performance, since that single test concludes how well a model adapts to this fixed configuration of changes, while the ultimate goal is to assess the adaptation to changes that happen unexpectedly. We propose a methodology for obtaining datasets for multiple tests by permuting the order of the original data. A random permutation is not suitable, as it makes the data distribution uniform over time and destroys the adaptive learning task. Therefore, we propose three controlled permutation techniques that make it possible to acquire new datasets by introducing restricted variations in the order of examples. The control mechanisms with theoretical guarantees of preserving distributions ensure that the new sets represent close variations of the original learning task. Complementary tests on such sets allow to analyze sensitivity of the performance to variations in how changes happen and this way enrich the assessment of adaptive supervised learning models.

Keywords

Concept drift Evaluation Data streams Permutations 

Notes

Acknowledgments

The research leading to these results has received funding from the European Commission within the Marie Curie Industry and Academia Partnerships and Pathways (IAPP) programme under grant agreement no. 251617.

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Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.Department of Information and Computer ScienceAalto UniversityEspooFinland
  2. 2.Helsinki Institute for Information Technology (HIIT)EspooFinland
  3. 3.Bournemouth UniversityPoole, DorsetUK

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