In this paper we propose a new method to reduce the size of Breiman’s Random Forests. Given a RandomForest and a target size, our algorithm builds a linear combination of trees which minimizes the training error. Selected trees, as well as weights of the linear combination are obtained by means of the Orthogonal Matching Pursuit algorithm. We test our method on many public benchmark datasets both on regression and binary classification, and we compare it to other pruning techniques. Experiments show that our technique performs significantly better or equally good on many datasets1. We also discuss the benefit and short-coming of learning weights for the pruned forest which lead us to propose to use a non-negative constraint on the OMP weights for better empirical results.

Luc Giffon, Charly Lamothe, Léo Bouscarrat, Paolo Milanesi, Farah Cherfaoui, and Sokol Ko, Pruning Random Forest with Orthogonal Matching Trees, Proc. of CAP 2020.

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Translating biomedical ontologies is an important challenge, but doing it manually requires much time and money. We study the possibility to use open-source knowledge bases to translate biomedical ontologies. We focus on two aspects: coverage and quality. We look at the coverage of two biomedical ontologies focusing on diseases with respect to Wikidata for 9 European languages (Czech, Dutch, English, French, German, Italian, Polish, Portuguese and Spanish) for both, plus Arabic, Chinese and Russian for the second. We first use direct links between Wikidata and the studied ontologies and then use second-order links by going through other intermediate ontologies. We then compare the quality of the translations obtained thanks to Wikidata with a commercial machine translation tool, here Google Cloud Translation.

Léo Bouscarrat, Antoine Bonnefoy, Cécile Capponi, Carlos Ramisch, Multilingual Enrichment of Disease Biomedical Ontologies, Proc. of MultilingualBIO 2020.

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