Improving the interpretability of classification rules discovered by an ant colony algorithm

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A Survey of Methods for Explaining Black Box Models (2018, background): “In this kind of model, a more honest measure could be the average number of conditions evaluated to classify a set of test records [84].”
Explainable Artificial Intelligence: a Systematic Review (2020, background): “An Ant Colony Optimization (ACO) algorithm was proposed in [269] to create interpretable rules.”
Comprehensible classification models: a position paper (2014, methodology): “This characteristic of rule sets has the disadvantage of requiring an extra method for resolving conflicting class predictions [9], [12], [51], [72] – which is not needed in a decision tree.”
A Survey on the Explainability of Supervised Machine Learning (2020, methodology): “The cAntMinerPB ordered algorithm uses ant-based induction to create an ordered list of rules that consider interactions between individual rules whereas its unordered version creates an unordered set of rules (Otero & Freitas, 2016).”