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Stream-Based Active Learning for Sliding Windows Under the Influence of Verification Latency

This repository provides the experiment framework for the article "Stream-Based Active Learning for Sliding Windows Under the Influence of Verification Latency", which is submitted to the ECMLPKDD Special Issue of the Journal Machine Learning published by Springer.


The following sources for the data embedded in the pickle files in the datasets folder were used:

luxembourg:

  • https://sites.google.com/site/zliobaite/resources-1
  • R. Jowell and the Central Coordinating Team. European social survey 2002/2003; 2004/2005; 2006/2007. Technical Reports, London: Centre for Comparative Social Surveys, City University, 2003, 2005, 2007.
  • Žliobaitė, I. (2011). Combining similarity in time and space for training set formation under concept drift. Intelligent Data Analysis 15(4), p. 589-611.

rialto & noaa_weather:

  • https://github.com/vlosing/driftDatasets
  • V. Losing, B. Hammer and H. Wersing, "KNN Classifier with Self Adjusting Memory for Heterogeneous Concept Drift," 2016 IEEE 16th International Conference on Data Mining (ICDM), 2016, pp. 291-300, doi: 10.1109/ICDM.2016.0040.

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