Monotonicity in Ant Colony Classification Algorithms

Brookhouse, James and Otero, Fernando E.B. (2016) Monotonicity in Ant Colony Classification Algorithms. In: 10th International Conference on Swarm Intelligence (ANTS 2016), 7-9 Sep 2016, Brussels, Belgium. (doi: (Full text available)


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.

Item Type: Conference or workshop item (Paper)
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Faculties > Sciences > School of Computing > Data Science
Depositing User: Fernando Otero
Date Deposited: 22 May 2016 22:00 UTC
Last Modified: 05 Jan 2018 11:35 UTC
Resource URI: (The current URI for this page, for reference purposes)
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