For mutation testing, the huge cost of running test suites on a large number of mutants has been a serious obstacle. To resolve this problem, we propose a learning-based mutant reduction technique MuTrain. MuTrain uses cost-considerate linear regression (i.e., CLARS) to learn a mutation model, which predicts the mutation score of a test suite based on the mutation testing results of a previous version of a target program. Then, MuTrain applies the mutation model for subsequent versions to predict mutation scores with significantly fewer mutants. For effective mutant reduction and accurate mutation score prediction, MuTrain uses fine-grained mutation operators refined from the existing coarse-grained mutation operators. The experiment results show that MuTrain reduces the number of mutants effectively (i.e., selecting only 1.6% of mutants). Moreover, MuTrain predicts mutation score far more accurately than the existing mutant reduction techniques and random mutant selection. We also found that MuTrain achieves much greater mutant reduction when it uses the fine-grained mutation operators than the traditional coarse-grained mutation operators (i.e., 1.6% vs. 14.6%).