About: Comparing Explanations between Random Forests and Artificial Neural Networks

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type	http://eprints.org/ontology/ConferenceItemEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #75472 Comparing Explanations between Random Forests and Artificial Neural Networks
sameAs	Comparing Explanations between Random Forests and Artificial Neural Networks
http://eprints.org/ontology/hasAccepted	Comparing Explanations between Random Forests and Artificial Neural Networks (PDF)
http://eprints.org/ontology/hasDocument	Comparing Explanations between Random Forests and Artificial Neural Networks (PDF) Comparing Explanations between Random Forests and Artificial Neural Networks (Other) Comparing Explanations between Random Forests and Artificial Neural Networks (Other) Comparing Explanations between Random Forests and Artificial Neural Networks (Other) Comparing Explanations between Random Forests and Artificial Neural Networks (Other) Comparing Explanations between Random Forests and Artificial Neural Networks (Other)
dc:hasVersion	Comparing Explanations between Random Forests and Artificial Neural Networks (PDF)
Title	Comparing Explanations between Random Forests and Artificial Neural Networks
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	2019-11-28
Creator	Lee Harris Marek Grzes
status	peer reviewed published
Publisher	IEEE
abstract	The decisions made by machines are increasingly comparable in predictive performance to those made by humans, but these decision making processes are often concealed as black boxes. Additional techniques are required to extract understanding, and one such category are explanation methods. This research compares the explanations of two popular forms of artificial intelligence; neural networks and random forests. Researchers in either field often have divided opinions on transparency, and comparing explanations may discover similar ground truths between models. Similarity can help to encourage trust in predictive accuracy alongside transparent structure and unite the respective research fields. This research explores a variety of simulated and real-world datasets that ensure fair applicability to both learning algorithms. A new heuristic explanation method that extends an existing technique is introduced, and our results show that this is somewhat similar to the other methods examined whilst also offering an alternative perspective towards least-important features.
Is Part Of	https://kar.kent.ac.uk/id/repository
Subject	QA76.87 Neural computers, neural networks
list of authors	https://kar.kent.ac.uk/id/eprint/75472#authors
presented at	2019 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2019)
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 #75472 Comparing Explanations between Random Forests and Artificial Neural Networks

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