  We study the problem of choosing the best subset of p features in linear
regression given n observations. This problem naturally contains two objective
functions including minimizing the amount of bias and minimizing the number of
predictors. The existing approaches transform the problem into a
single-objective optimization problem. We explain the main weaknesses of
existing approaches, and to overcome their drawbacks, we propose a bi-objective
mixed integer linear programming approach. A computational study shows the
efficacy of the proposed approach.
