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Table 2 Effects of abstaining, stabilization and discretization on prediction accuracy. The table shows different experimental setups to test the effects of abstaining, stabilization, and the new discretization technique. All the experiments involve 10-fold cross-validation on the ESR data set. Column 1 represents the number of target genes used. Column 2 indicates the number of iterations. Column 3 specifies the discretization technique used. Columns 4 and 5 specify whether abstaining and stabilization were used. Column 6 reports the average test loss. The first and fifth experiments are the original GeneClass algorithm. The fourth and eighth experiments are the Robust GeneClass algorithm with all three algorithmic improvements. Comparing rows 2, 3 and 6, 7 we see that the use of new discretization technique alone improves test loss. Comparing rows 3, 4 and 7, 8 we see that stabilization alone does not have any significant effect on test loss. Comparing rows 1, 2 and 5, 6 we observe that abstaining increases test loss. The combined effect of the three algorithmic improvements results in a small increase in test loss due to the somewhat poorer accuracy of abstaining weak rules; however, abstaining improves the interpretability of the model and significantly speeds up training time.

From: A classification-based framework for predicting and analyzing gene regulatory response

No. of targets No. of Iterations Discretization Abstain Stabilize Mean test-loss
1411 400 Old No No 11.50%
1411 400 Old Yes No 16.01%
1411 400 New Yes No 13.13%
1411 400 New Yes Yes 13.50%
5579 1000 Old No No 14.00%
5579 1000 Old Yes No 18.99%
5579 1000 New Yes No 15.80%
5579 1000 New Yes Yes 16.10%