Normally, when using an ensemble method, such as baggin or boosting, in binary classification, there is a reqauirment that each weak classifier have accuracy better than 50%.
In the multiclass claaification setting, this is often infeasible. Is there a way to improve upon multiclass classification with ensembles.
For an example to make this concrete: Say I have a problem with 1000 classes, and I train 50 models, each with 10% accuracy, which is 100x better than random guessing.
Is there a way to combine these models to form a better classification algorithm?