There are many machine learning algorithms currently available. In the 21st century, the problem no longer lies in writing the learner, but in choosing which learners to run on a given data set. In this paper, we argue that the final choice of learners should not be exclusive; in fact, there are distinct advantages in running data sets through multiple learners.

To illustrate our point, we perform a case study on a reuse data set using three different styles of learners: association rule, decision tree induction, and treatment. Software reuse is a topic of avid debate in the professional and academic arena; it has proven that it can be both a blessing and a curse. Although there is much debate over where and when reuse should be instituted into a project, our learners found some procedures which should significantly improve the odds of a reuse program succeeding.