  The Set Covering Machine (SCM) is a greedy learning algorithm that produces
sparse classifiers. We extend the SCM for datasets that contain a huge number
of features. The whole genetic material of living organisms is an example of
such a case, where the number of feature exceeds 10^7. Three human pathogens
were used to evaluate the performance of the SCM at predicting antimicrobial
resistance. Our results show that the SCM compares favorably in terms of
sparsity and accuracy against L1 and L2 regularized Support Vector Machines and
CART decision trees. Moreover, the SCM was the only algorithm that could
consider the full feature space. For all other algorithms, the latter had to be
filtered as a preprocessing step.
