1. Evaluating classification algorithms applied to data streams Author: Ing. Esteban D. Donato Advisor: Dr. Fazel Famili Co-Advisor: Dra. Ana S. Haedo Dec-2009 Maestría en Explotación de Datos y Descubrimiento del Conocimiento

14. Data sets generated Dataset with no concept drift, outlier of noise Dataset with 10% of noisy data Dataset with 1% of outliers Dataset with 3 concept drift

15. Results Capacity to detect and respond to concept drift

16. Results Capacity to detect and respond to virtual concept drift

17. Results Capacity to detect and respond to recurring concept drift

18. Results Capacity to adapt to sudden concept drift

19. Results Capacity to adapt to gradual concept drift

20. Results Capacity to adapt to frequent concept drift

21. Results Accuracy of the classification task VFDTc (CA) VFDTc (EBP) UFFT CVFDT measures derived from the confusion matrix Predicted Predicted Class 1 Class 2 Actual Class 1 44.5% (887) 5.5% (109) Actual Class 2 5% (101) 45% (903) Predicted Predicted Class 1 Class 2 Actual Class 1 39% (777) 11% (219) Actual Class 2 9% (173) 41% (831) Predicted Predicted Class 1 Class 2 Actual Class 1 46% (928) 3.5% (68) Actual Class 2 2.5% (48) 48% (956) Predicted Predicted Class 1 Class 2 Actual Class 1 34.5% (685) 15.5% (311) Actual Class 2 15.5% (312) 34.5% (692) Accuracy (AC) True positive (TP) False Positive (FP) True Negative (TN) False Negative (FN) Precision (P) VFDTc (CA) 0.89 0.89 0.10 0.90 0.11 0.90 VFDTc (EBP) 0.80 0.78 0.17 0.83 0.22 0.82 UFFT 0.94 0.93 0.05 0.95 0.07 0.95 CVFDT 0.69 0.69 0.31 0.69 0.31 0.69

22. Results Dealing with outliers

23. Results Dealing with noisy data

24. Results Speed (Time to take to process an item in the stream)