About: A new approach for interpreting Random Forest models and its application to the biology of ageing

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type	http://eprints.org/ontology/ArticleEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #66666 A new approach for interpreting Random Forest models and its application to the biology of ageing
sameAs	A new approach for interpreting Random Forest models and its application to the biology of ageing
http://eprints.org/ontology/hasDocument	A new approach for interpreting Random Forest models and its application to the biology of ageing (PDF) A new approach for interpreting Random Forest models and its application to the biology of ageing (Other) A new approach for interpreting Random Forest models and its application to the biology of ageing (Other) A new approach for interpreting Random Forest models and its application to the biology of ageing (Other) A new approach for interpreting Random Forest models and its application to the biology of ageing (Other) A new approach for interpreting Random Forest models and its application to the biology of ageing (Other)
http://eprints.org/ontology/hasPublished	A new approach for interpreting Random Forest models and its application to the biology of ageing (PDF)
dc:hasVersion	A new approach for interpreting Random Forest models and its application to the biology of ageing (PDF)
Title	A new approach for interpreting Random Forest models and its application to the biology of ageing
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	2018-02-16
Creator	Daniel Palmer Aoife Doherty João Pedro de Magalhães A. Freitas Fabio Fabris
status	peer reviewed published
Publisher	Oxford University Press
abstract	This work uses the Random Forest (RF) classification algorithm to predict if a gene is over-expressed, under-expressed or has no change in expression with age in the brain. RFs have high predictive power, and RF models can be interpreted using a feature (variable) importance measure. However, current feature importance measures evaluate a feature as a whole (all feature values). We show that, for a popular type of biological data (Gene Ontology-based), usually only one value of a feature is particularly important for classification and the interpretation of the RF model. Hence, we propose a new algorithm for identifying the most important and most informative feature values in an RF model.
Is Part Of	Bioinformatics https://kar.kent.ac.uk/id/repository
Subject	Q335 Artificial intelligence
list of authors	https://kar.kent.ac.uk/id/eprint/66666#authors
issue	14
volume	34
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 #66666 A new approach for interpreting Random Forest models and its application to the biology of ageing

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