  Understanding how multiple features are associated and contribute to a
specific objective is as important as understanding how each feature
contributes to a particular outcome. Interpretability of a single feature in a
prediction may be handled in multiple ways; however, in a multi-objective
prediction, it is difficult to obtain interpretability of a combination of
feature values. To address this issue, we propose an objective specific feature
interaction design using multi-labels to find the optimal combination of
features in agricultural settings. One of the novel aspects of this design is
the identification of a method that integrates feature explanations with global
sensitivity analysis in order to ensure combinatorial optimization in
multi-objective settings. We have demonstrated in our preliminary experiments
that an approximate combination of feature values can be found to achieve the
desired outcome using two agricultural datasets: one with pre-harvest poultry
farm practices for multi-drug resistance presence, and one with post-harvest
poultry farm practices for food-borne pathogens. In our combinatorial
optimization approach, all three pathogens are taken into consideration
simultaneously to account for the interaction between conditions that favor
different types of pathogen growth. These results indicate that
explanation-based approaches are capable of identifying combinations of
features that reduce pathogen presence in fewer iterations than a baseline.
