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Human in the loop fuzzy pattern tree evolution

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Show simple item record Murphy, Aidan Murphy, Gráinne Dias, Douglas Mota Amaral, Jorge Naredo, Enrique Ryan, Conor 2022-02-22T16:26:41Z 2022-02-22T16:26:41Z 2022
dc.description peer-reviewed en_US
dc.description.abstract Fuzzy pattern trees evolved using grammatical evolution, a system we call Fuzzy Grammatical Evolution, are shown to be a robust Explainable Artificial Intelligence technique. Experimental results show Fuzzy Grammatical Evolution achieves competitive results when compared against SVM, Random Forest and Logistic Regression on a set of real world benchmark problems. Fuzzy Grammatical Evolution allows for human input throughout the evolutionary process. Regularization methods and double tournament selection were investigated to determine what method was most successful at finding smaller, more interpretable models. A domain expert was recruited to investigate the interpretability of the models found and to give a confidence score for each model. This expert successfully identified overfit models, validating that Fuzzy Grammatical Evolution can be regarded a powerful Explainable Artificial Intelligence technique. en_US
dc.language.iso eng en_US
dc.publisher Springer en_US
dc.relation.ispartofseries SN Computer Science;3,163
dc.subject Grammatical evolution en_US
dc.subject Fuzzy logic en_US
dc.title Human in the loop fuzzy pattern tree evolution en_US
dc.type info:eu-repo/semantics/article en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.identifier.doi 10.1007/s42979-022-01044-w
dc.contributor.sponsor IReL Consortium en_US
dc.contributor.sponsor Lero en_US
dc.contributor.sponsor Coordenação de Aperfeiçoamento de Pessoal de Nível Superior en_US
dc.relation.projectid 13/RC/2094 en_US
dc.relation.projectid 16/IA/4605 en_US
dc.relation.projectid 001 en_US
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US

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