An empirical solution for over-pruning with a novel ensemble-learning method for fMRI decoding

To the best of our knowledge, ours is the first study to propose a solution for over-pruning with ensemble-learning that is applicable to any sparse algorithm. In addition, from the viewpoint of machine learning, we provide the novel idea of using the sparse classification algorithm to generate accurate divergent base classifiers.