The challenge is now over. But it remains open for post-challenge submissions!
Submitted by Erinija
An ensemble of linear discriminants is identified by splitting the training data n times into training and monitoring. The liknon is solved with increasing values of regularization parameter, the best model of the individual split is selected based on minimum monitoring error. The weights of the discriminants identify useful features. Other rules were trained on the selected feature subsets. Only for Gina Knn3 otperformed the ensemble.
These models were of median performance in 10-fold crossvalidation
gina8 hiva3 ada8 sylva4 nova9
predicted number of misclassifications: 1964, 9151, 7444, 2565, 1403
|Dataset||Balanced Error||Area Under Curve|
This entry is a complete agnostic learning entry.