End-to-end Feature Selection Approach for Learning Skinny Trees

• URL: http://arxiv.org/abs/2310.18542v2
• Date: Tue, 3 Sep 2024 07:34:54 GMT
• Title: End-to-end Feature Selection Approach for Learning Skinny Trees
• Authors: Shibal Ibrahim, Kayhan Behdin, Rahul Mazumder,
• Abstract summary: We propose a new optimization-based approach for feature selection in tree ensembles. Skinny Trees is an end-to-end toolkit for feature selection in tree ensembles.
• Score: 13.388576838688202
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a new optimization-based approach for feature selection in tree ensembles, an important problem in statistics and machine learning. Popular tree ensemble toolkits e.g., Gradient Boosted Trees and Random Forests support feature selection post-training based on feature importance scores, while very popular, they are known to have drawbacks. We propose Skinny Trees: an end-to-end toolkit for feature selection in tree ensembles where we train a tree ensemble while controlling the number of selected features. Our optimization-based approach learns an ensemble of differentiable trees, and simultaneously performs feature selection using a grouped $\ell_0$-regularizer. We use first-order methods for optimization and present convergence guarantees for our approach. We use a dense-to-sparse regularization scheduling scheme that can lead to more expressive and sparser tree ensembles. On 15 synthetic and real-world datasets, Skinny Trees can achieve $1.5\!\times\! -~620~\!\times\!$ feature compression rates, leading up to $10\times$ faster inference over dense trees, without any loss in performance. Skinny Trees lead to superior feature selection than many existing toolkits e.g., in terms of AUC performance for 25\% feature budget, Skinny Trees outperforms LightGBM by $10.2\%$ (up to $37.7\%$), and Random Forests by $3\%$ (up to $12.5\%$).

Related papers

• Joint Optimization of Piecewise Linear Ensembles [11.34717731050474]
  Tree ensembles achieve state-of-the-art performance on numerous prediction tasks. We propose $textbfJ$oint $textbfO$ptimization of $textbfL$inear $textbfEn$sembles (JOPLEn) JOPLEn allows several common penalties, including sparsity-promoting and subspace-norms, to be applied to nonlinear prediction.
  arXiv  Detail & Related papers  (2024-05-01T03:59:06Z)
• Learning a Decision Tree Algorithm with Transformers [75.96920867382859]
  We introduce MetaTree, a transformer-based model trained via meta-learning to directly produce strong decision trees. We fit both greedy decision trees and globally optimized decision trees on a large number of datasets, and train MetaTree to produce only the trees that achieve strong generalization performance.
  arXiv  Detail & Related papers  (2024-02-06T07:40:53Z)
• MAPTree: Beating "Optimal" Decision Trees with Bayesian Decision Trees [2.421336072915701]
  We present a Bayesian approach to decision tree induction via maximum a posteriori inference of a posterior distribution over trees. We propose an AND/OR search algorithm, dubbed MAPTree, which is able to recover the maximum a posteriori tree.
  arXiv  Detail & Related papers  (2023-09-26T23:43:37Z)
• On Computing Optimal Tree Ensembles [7.424944196676223]
  Random forests and, more generally, (decisionnobreakdash-)tree ensembles are widely used methods for classification and regression. Recent algorithmic advances allow to compute decision trees that are optimal for various measures such as their size or depth. We provide two novel algorithms and corresponding lower bounds.
  arXiv  Detail & Related papers  (2023-06-07T13:30:43Z)
• ForestPrune: Compact Depth-Controlled Tree Ensembles [7.538482310185135]
  We present ForestPrune, a novel framework to post-process tree ensembles by pruning depth layers from individual trees. We develop a specialized optimization algorithm to efficiently obtain high-quality solutions to problems under ForestPrune. Our experiments demonstrate that ForestPrune produces parsimonious models that outperform models extracted by existing post-processing algorithms.
  arXiv  Detail & Related papers  (2022-05-31T22:04:18Z)
• Social Interpretable Tree for Pedestrian Trajectory Prediction [75.81745697967608]
  We propose a tree-based method, termed as Social Interpretable Tree (SIT), to address this multi-modal prediction task. A path in the tree from the root to leaf represents an individual possible future trajectory. Despite the hand-crafted tree, the experimental results on ETH-UCY and Stanford Drone datasets demonstrate that our method is capable of matching or exceeding the performance of state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-05-26T12:18:44Z)
• Growing Deep Forests Efficiently with Soft Routing and Learned Connectivity [79.83903179393164]
  This paper further extends the deep forest idea in several important aspects. We employ a probabilistic tree whose nodes make probabilistic routing decisions, a.k.a., soft routing, rather than hard binary decisions. Experiments on the MNIST dataset demonstrate that our empowered deep forests can achieve better or comparable performance than [1],[3].
  arXiv  Detail & Related papers  (2020-12-29T18:05:05Z)
• An Efficient Adversarial Attack for Tree Ensembles [91.05779257472675]
  adversarial attacks on tree based ensembles such as gradient boosting decision trees (DTs) and random forests (RFs) We show that our method can be thousands of times faster than the previous mixed-integer linear programming (MILP) based approach. Our code is available at https://chong-z/tree-ensemble-attack.
  arXiv  Detail & Related papers  (2020-10-22T10:59:49Z)
• Learning Binary Decision Trees by Argmin Differentiation [34.9154848754842]
  We learn binary decision trees that partition data for some downstream task. We do so by relaxing a mixed-integer program for the discrete parameters. We derive customized algorithms to efficiently compute the forward and backward passes.
  arXiv  Detail & Related papers  (2020-10-09T15:11:28Z)
• MurTree: Optimal Classification Trees via Dynamic Programming and Search [61.817059565926336]
  We present a novel algorithm for learning optimal classification trees based on dynamic programming and search. Our approach uses only a fraction of the time required by the state-of-the-art and can handle datasets with tens of thousands of instances.
  arXiv  Detail & Related papers  (2020-07-24T17:06:55Z)
• Generalized and Scalable Optimal Sparse Decision Trees [56.35541305670828]
  We present techniques that produce optimal decision trees over a variety of objectives. We also introduce a scalable algorithm that produces provably optimal results in the presence of continuous variables.
  arXiv  Detail & Related papers  (2020-06-15T19:00:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.