http://ceur-ws.org/Vol-1180/CLEF2014wn-Pan-FreryEt2014.pdfInternational audienceThis article describes our proposal for the Author Identification task in the PAN CLEF Challenge 2014. We have adopted a machine learning ap- proach based on several representations of the texts and on optimized decision trees which have as entry various attributes and which are learned for every train- ing corpus separately for this classification task. Our method ranked us at the 2nd place with an overall AUC of 70.7%, and C@1 of 68.4% and, between the 1st and the 6th place on the six corpora
