Year: 2024
Type: conference-paper
Abstract: Tree ensembles achieve state-of-the-art performance on numerous prediction tasks. We propose Joint Optimization of Piecewise Linear Ensembles (JOPLEn), which jointly fits piecewise linear models at all leaf nodes of an existing tree ensemble. In addition to enhancing the ensemble expressiveness, JOPLEn allows several common penalties, including sparsity-promoting and subspace-norms, to be applied to nonlinear prediction. For example, JOPLEn with a nuclear norm penalty learns subspace-aligned functions. Additionally, JOPLEn (combined with a Dirty LASSO penalty) is an effective feature selection method for nonlinear prediction in multitask learning. Finally, we demonstrate the performance of JOPLEn on 153 regression and classification datasets and with a variety of penalties. JOPLEn leads to improved prediction performance relative to not only standard random forest and boosted tree ensembles, but also other methods for enhancing tree ensembles.
Authors: Matt Raymond, Angela Violi, Clayton Scott
Institution: University of Michigan
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4404037363