Ghyslain Gagnon
Ghyslain Gagnon
B.Ing., M.Ing. (ÉTS), Ph.D. (Carleton)
Département de génie électrique

Synthetic Data Sets for MIL
These data sets are used to assess the performance of MIL algorithms at different witness rates, levels of positive class complexity and number of noisy features. The data set was introduced in:
M.-A. Carbonneau, E. Granger, A. J. Raymond, and G. Gagnon, “Robust Multiple-Instance Learning Ensembles Using Random Subspace Instance Selection,” Pattern Recognition, 2016.

Please cite this paper whenever the data set is used.
Here's the link to get the data:
[get the data set]

or, alternatively:

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