Abstract
Classification algorithms generally do not use existing domain knowledge during model construction. The creation of models that conflict with existing knowledge can reduce model acceptance, as users have to trust the models they use. Domain knowledge can be integrated into algorithms using semantic constraints to guide model construction. This paper proposes an extension to an existing ACO-based classification rule learner to create lists of monotonic classification rules. The proposed algorithm was compared to a majority classifier and the Ordinal Learning Model (OLM) monotonic learner. Our results show that the proposed algorithm successfully outperformed OLM’s predictive accuracy while still producing monotonic models.
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Notes
- 1.
An example is covered by a rule when it satisfies all terms (attribute-value conditions) in the antecedent of the rule.
- 2.
ACO-based algorithms therefore run a total of 50 times before the average is taken.
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Brookhouse, J., Otero, F.E.B. (2016). Monotonicity in Ant Colony Classification Algorithms. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2016. Lecture Notes in Computer Science(), vol 9882. Springer, Cham. https://doi.org/10.1007/978-3-319-44427-7_12
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DOI: https://doi.org/10.1007/978-3-319-44427-7_12
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