  The popular task of 3D human action recognition is almost exclusively solved
by training deep-learning classifiers. To achieve a high recognition accuracy,
the input 3D actions are often pre-processed by various normalization or
augmentation techniques. However, it is not computationally feasible to train a
classifier for each possible variant of training data in order to select the
best-performing subset of pre-processing techniques for a given dataset. In
this paper, we propose to train an independent classifier for each available
pre-processing technique and fuse the classification results based on a strict
majority vote rule. Together with a proposed evaluation procedure, we can very
efficiently determine the best combination of normalization and augmentation
techniques for a specific dataset. For the best-performing combination, we can
retrospectively apply the normalized/augmented variants of input data to train
only a single classifier. This also allows us to decide whether it is better to
train a single model, or rather a set of independent classifiers.
