 A common approach for multi-class classification is to learn pairwise models between every pair of classes (here algorithms) 
and choose the class that was predicted most often by the pairwise models.We can weight the instances of the pairwise prediction problem by the performance difference between the two algorithms.This is motivated by the fact that we care most about getting predictions with large differences correct, but the penalty for an incorrect prediction is small if there is almost no performance difference.