{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T21:56:03Z","timestamp":1781214963420,"version":"3.54.1"},"reference-count":10,"publisher":"Oxford University Press (OUP)","issue":"15","license":[{"start":{"date-parts":[[2021,2,1]],"date-time":"2021-02-01T00:00:00Z","timestamp":1612137600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Institut de Calcul Intensif","award":["#OG1811080\/2019"],"award-info":[{"award-number":["#OG1811080\/2019"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,8,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>The principle of Breiman's random forest (RF) is to build and assemble complementary classification trees in a way that maximizes their variability. We propose a new type of random forest that disobeys Breiman\u2019s principles and involves building trees with no classification errors in very large quantities. We used a new type of decision tree that uses a neuron at each node as well as an in-innovative half Christmas tree structure. With these new RFs, we developed a score, based on a family of ten new statistical information criteria, called Nguyen information criteria (NICs), to evaluate the predictive qualities of features in three dimensions.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>The first NIC allowed the Akaike information criterion to be minimized more quickly than data obtained with the Gini index when the features were introduced in a logistic regression model. The selected features based on the NICScore showed a slight advantage compared to the support vector machines\u2014recursive feature elimination (SVM-RFE) method. We demonstrate that the inclusion of artificial neurons in tree nodes allows a large number of classifiers in the same node to be taken into account simultaneously and results in perfect trees without classification errors.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The methods used to build the perfect trees in this article were implemented in the \u2018ROP\u2019 R package, archived at https:\/\/cran.r-project.org\/web\/packages\/ROP\/index.html.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab074","type":"journal-article","created":{"date-parts":[[2021,1,27]],"date-time":"2021-01-27T20:11:14Z","timestamp":1611778274000},"page":"2165-2174","source":"Crossref","is-referenced-by-count":52,"title":["Random forest of perfect trees: concept, performance, applications and perspectives"],"prefix":"10.1093","volume":"37","author":[{"given":"Jean-Michel","family":"Nguyen","sequence":"first","affiliation":[{"name":"Techniques de l\u2019Ing\u00e9nierie M\u00e9dicale et de la Complexit\u00e9 - Informatique, Math\u00e9matiques, Applications (TIMC\u2014IMAG) -UMR 5525, Universit\u00e9 Grenoble Alpes\u2014CNRS , France"},{"name":"CRCINA - INCIT Department - Team 2 - 8 , quai Moncousu - BP 70721 - 44007 Nantes cedex 1 , France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pascal","family":"J\u00e9z\u00e9quel","sequence":"additional","affiliation":[{"name":"Institut de Canc\u00e9rologie de l\u2019Ouest, Bd Jacques Monod, Unit\u00e9 de Bioinfomique , Saint Herblain Cedex, 44805, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pierre","family":"Gillois","sequence":"additional","affiliation":[{"name":"Techniques de l\u2019Ing\u00e9nierie M\u00e9dicale et de la Complexit\u00e9 - Informatique, Math\u00e9matiques, Applications (TIMC\u2014IMAG) -UMR 5525, Universit\u00e9 Grenoble Alpes\u2014CNRS , France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luisa","family":"Silva","sequence":"additional","affiliation":[{"name":"\u00c9cole Centrale de Nantes, High Performance Computing Institute , Nantes Cedex 3, 44321, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Faouda","family":"Ben Azzouz","sequence":"additional","affiliation":[{"name":"Institut de Canc\u00e9rologie de l\u2019Ouest, Bd Jacques Monod, Unit\u00e9 de Bioinfomique , Saint Herblain Cedex, 44805, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sophie","family":"Lambert-Lacroix","sequence":"additional","affiliation":[{"name":"D\u00e9partement STID, IUT2 de Grenoble\u2014Universit\u00e9 Grenoble Alpes , St Martin d\u2019Heres, 38400, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philippe","family":"Juin","sequence":"additional","affiliation":[{"name":"CRCINA, INSERM, CNRS, Universit\u00e9 de Nantes, Universit\u00e9 d'Angers, Institut de Recherche en Sant\u00e9-Universit\u00e9 de Nantes , Nantes Cedex 1, 44007, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mario","family":"Campone","sequence":"additional","affiliation":[{"name":"Oncologie M\u00e9dicale, Institut de Canc\u00e9rologie de l\u2019Ouest\u2014Ren\u00e9 Gauducheau , Saint Herblain Cedex, 44805, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aur\u00e9lie","family":"Gaultier","sequence":"additional","affiliation":[{"name":"Nantes Department of General Practice , 1 rue G. 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