About: An Evolutionary Algorithm for Learning Interpretable Ensembles of Classifiers

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type	http://eprints.org/ontology/BookSectionEPrint http://eprints.org/ontology/EPrint Article Book Section
seeAlso	HTML Summary of #84754 An Evolutionary Algorithm for Learning Interpretable Ensembles of Classifiers
sameAs	An Evolutionary Algorithm for Learning Interpretable Ensembles of Classifiers
http://www.loc.gov...erms/relators/EDT	Ronaldo C. Prati Ricardo Cerri
http://eprints.org/ontology/hasAccepted	An Evolutionary Algorithm for Learning Interpretable Ensembles of Classifiers (PDF)
http://eprints.org/ontology/hasDocument	An Evolutionary Algorithm for Learning Interpretable Ensembles of Classifiers (PDF) An Evolutionary Algorithm for Learning Interpretable Ensembles of Classifiers (Other) An Evolutionary Algorithm for Learning Interpretable Ensembles of Classifiers (Other) An Evolutionary Algorithm for Learning Interpretable Ensembles of Classifiers (Other) An Evolutionary Algorithm for Learning Interpretable Ensembles of Classifiers (Other) An Evolutionary Algorithm for Learning Interpretable Ensembles of Classifiers (Other)
dc:hasVersion	An Evolutionary Algorithm for Learning Interpretable Ensembles of Classifiers (PDF)
Title	An Evolutionary Algorithm for Learning Interpretable Ensembles of Classifiers
described by	https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3 https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
Date	2020-10-13
Creator	Rodrigo C. Barros A. Freitas Henry E. L. Cagnini
status	peer reviewed published
Publisher	Springer
abstract	Ensembles of classifiers are a very popular type of method for performing classification, due to their usually high predictive accuracy. However, ensembles have two drawbacks. First, ensembles are usually considered a ‘black box’, non-interpretable type of classification model, mainly because typically there are a very large number of classifiers in the ensemble (and often each classifier in the ensemble is a black-box classifier by itself). This lack of interpretability is an important limitation in application domains where a model’s predictions should be carefully interpreted by users, like medicine, law, etc. Second, ensemble methods typically involve many hyper-parameters, and it is difficult for users to select the best settings for those hyper-parameters. In this work we propose an Evolutionary Algorithm (an Estimation of Distribution Algorithm) that addresses both these drawbacks. This algorithm optimizes the hyper-parameter settings of a small ensemble of 5 interpretable classifiers, which allows users to interpret each classifier. In our experiments, the ensembles learned by the proposed Evolutionary Algorithm achieved the same level of predictive accuracy as a well-known Random Forest ensemble, but with the benefit of learning interpretable models (unlike Random Forests).
Is Part Of	https://kar.kent.ac.uk/id/repository
Subject	QA 76 Software, computer programming,
list of authors	https://kar.kent.ac.uk/id/eprint/84754#authors
list of editors	https://kar.kent.ac.uk/id/eprint/84754#editors
volume	12319
is topic of	https://www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3 https://raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #84754 An Evolutionary Algorithm for Learning Interpretable Ensembles of Classifiers

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