OR can help you in what you are doing. The article you mentioned can help you to set the coefficient and also to select the variables (best subset selection) -- among other things (e.g., sparsity, robustness). This other article is probably also worth looking at, since it focuses solely on features selection. It is a rather comprehensive approach. However, you should know that it results in a mixed-integer quadratic programming problem. With a too large dataset the resulting problem might become too difficult to solve.
The alternative is, probably, trial and error: you define different models with different subsets of variables and compare them. Libraries for this are widely available in, e.g., Python (scikit-learn), R, or even MS Excel.
Compared with the above mentioned method, this is a heuristic approach: you may not find the best model. Nevertheless, it should be easier from a computational perspective, or at least scale better with the size of the dataset.