Celine Vens

TL;DR: HMC trees outperform HSC and SC trees along three dimensions: predictive accuracy, model size, and induction time, and it is concluded that HMC trees should definitely be considered in HMC tasks where interpretable models are desired.

TL;DR: This paper develops methods for learning two types of ensembles (bagging and random forests) of predictive clustering trees for global and local predictions of different types of structured outputs, and proposes to build ensemble models consisting of predictive clustered trees, which generalize classification trees.

TL;DR: Results of a crowdsourcing competition show that it is possible to accurately predict and reverse-engineer the smell of a molecule, with a predictive accuracy that closely approaches a key theoretical limit.

TL;DR: The results suggest that decision tree based methods are a state-of-the-art, efficient and easy-to-use approach to ORF function prediction.

TL;DR: This paper considers two ensemble learning techniques, bagging and random forests, and applies them to multi-objective decision trees (MODTs), which are decision trees that predict multiple target attributes at once and concludes that ensembles of MODTs yield better predictive performance than MODTs and are equally good, or better than ensembled of single-objectives decision trees.