Mutation testing is one of the strongest code-based test criteria. However, it is expensive as it involves a large number of mutants. To deal with this issue we propose a machine learning approach that learns to select fault revealing mutants. Fault revealing mutants are valuable to testers as their killing results in (collateral) fault revelation. We thus, formulate mutant reduction as the problem of selecting the mutants that are most likely to lead to test cases that uncover unknown program faults. We tackle this problem using a set of static program features and machine learning. Experimental results involving 1,629 real faults show that our approach reveals 14% to 18% more faults than a random mutant selection baseline.