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Monotonicity in Ant Colony Classification Algorithms

Brookhouse, James, Otero, Fernando E.B. (2016) Monotonicity in Ant Colony Classification Algorithms. In: Swarm Intelligence: Proceedings of 10th International Conference, ANTS 2016. 10th International Conference on Swarm Intelligence (ANTS 2016). Lecture Notes in Computer Science . pp. 137-148. Springer, Cham, Switzerland ISBN 978-3-319-44426-0. E-ISBN 978-3-319-44426-0. (doi:10.1007/978-3-319-44427-7_12) (KAR id:55662)


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)
DOI/Identification number: 10.1007/978-3-319-44427-7_12
Uncontrolled keywords: ant colony optimization; semantic constraints; monotonic; data mining; classification rules; sequential covering
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Fernando Otero
Date Deposited: 22 May 2016 22:00 UTC
Last Modified: 09 Dec 2022 01:17 UTC
Resource URI: (The current URI for this page, for reference purposes)

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