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

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type	http://eprints.org/ontology/ConferenceItemEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #67177 Post-Processing Methods to Enforce Monotonic Constraints in Ant Colony Classification Algorithms
sameAs	Post-Processing Methods to Enforce Monotonic Constraints in Ant Colony Classification Algorithms
http://eprints.org/ontology/hasAccepted	Post-Processing Methods to Enforce Monotonic Constraints in Ant Colony Classification Algorithms (PDF)
http://eprints.org/ontology/hasDocument	Post-Processing Methods to Enforce Monotonic Constraints in Ant Colony Classification Algorithms (PDF) Post-Processing Methods to Enforce Monotonic Constraints in Ant Colony Classification Algorithms (Other) Post-Processing Methods to Enforce Monotonic Constraints in Ant Colony Classification Algorithms (Other) Post-Processing Methods to Enforce Monotonic Constraints in Ant Colony Classification Algorithms (Other) Post-Processing Methods to Enforce Monotonic Constraints in Ant Colony Classification Algorithms (Other) Post-Processing Methods to Enforce Monotonic Constraints in Ant Colony Classification Algorithms (Other)
dc:hasVersion	Post-Processing Methods to Enforce Monotonic Constraints in Ant Colony Classification Algorithms (PDF)
Title	Post-Processing Methods to Enforce Monotonic Constraints in Ant Colony Classification Algorithms
described by	https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
Date	2018-07-08
Creator	Fernando E.B. Otero James Brookhouse
status	peer reviewed published
Publisher	IEEE
abstract	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.
Is Part Of	Neural Networks (IJCNN), The 2013 International Joint Conference https://kar.kent.ac.uk/id/repository
Subject	Q335 Artificial intelligence
list of authors	https://kar.kent.ac.uk/id/eprint/67177#authors
presented at	2018 International Joint Conference on Neural Networks
is topic of	https://raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #67177 Post-Processing Methods to Enforce Monotonic Constraints in Ant Colony Classification Algorithms

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