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Post-Processing Methods to Enforce Monotonic Constraints in Ant Colony Classification Algorithms

Brookhouse, James, Otero, Fernando E.B. (2018) Post-Processing Methods to Enforce Monotonic Constraints in Ant Colony Classification Algorithms. In: Proceedings of International Joint Conference on Neural Networks. 2018 International Joint Conference on Neural Networks (IJCNN). . pp. 1-8. IEEE, USA ISBN 978-1-5090-6015-3. E-ISBN 978-1-5090-6014-6. (doi:10.1109/IJCNN.2018.8489543) (KAR id:67177)

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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.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1109/IJCNN.2018.8489543
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Fernando Otero
Date Deposited: 31 May 2018 09:47 UTC
Last Modified: 16 Feb 2021 13:55 UTC
Resource URI: (The current URI for this page, for reference purposes)
Brookhouse, James:
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