Post-Processing Methods To Enforce Monotonic Constraints In Ant Colony Classification Algorithms

Most classification algorithms ignore existing domain knowledge during model construction, which can decrease the model's comprehensibility and increase the likelihood of model rejection due to users losing trust in the models they use. One approach to encapsulate this domain knowledge is monotonic constraints. This paper proposes new monotonic pruners to enforce monotonic constraints on models created by an existing ACO algorithm in a post-processing stage. We compare the effectiveness of the new pruners against an existing post-processing approach that also enforce constraints. Additionally, we also compare the effectiveness of both these post-processing procedures in isolation and in conjunction with favouring constraints in the learning phase. Our results show that our proposed pruners outperform the existing post-processing approach and the combination of favouring and enforcing constraints at different stages of the model construction process is the most effective solution.