arXiv:2606. 18853v1 Announce Type: cross Abstract: A recent line of work has reframed individual decision trees as linear models on engineered features associated with their splits, opening routes for oracle inequalities and feature-importance reinterpretation, but leaving open the question of what unified geometric object a forest induces when one indexes its feature map by nodes rather than by splits.
By Nicolas Mahler
arXiv:2606. 29326v1 Announce Type: cross Abstract: Gradient boosting in the form of decision tree ensembles has successfully been applied to a variety of problems using simple objective functions based on log-likelihoods of a single variable.
By David Cortes
arXiv:2609.10334v1 Announce Type: new
Abstract: This paper addresses the issue of supervised classification in the context of hyperspectral satellite images. It deals with two fundamental aspects: di...
By Mohamed Cherifi, Ammar Mesloub, Mohammed Nabil El Korso, Tayeb Touhami, Abdennour Hacine Gharbi
arXiv:2502. 00168v5 Announce Type: replace-cross Abstract: Supervised dimensionality reduction maps labeled data into a low-dimensional feature space while preserving class separation.
By Daniel Herrera-Esposito, Johannes Burge
The paper introduces DICS, a clustering-based framework that uses data-informed priors to construct a compact set of candidate splits for decision tree classifiers. By incorporating class-aware structure, DICS reduces the split search space, preserving predictive performance while cutting training time. The authors provide theoretical analysis and experimental results showing comparable accuracy to exhaustive search across synthetic and benchmark datasets.
arXiv:2608. 20258v1 Announce Type: new Abstract: Decision tree-based models are widely used in machine learning due to their interpretability and strong empirical performance.
By MD Saifur Rahman Mazumder, Feng Yu
The paper revisits Breiman’s insight that lowering inter‑tree correlation can boost random forest performance. It introduces two new variants—Dirichlet‑Multinomial Bagging Random Forest (DM) and Dirichlet‑Weighted Random Forest (DW)—which adjust sample reweighting through a concentration parameter α>0. A theoretical criterion is presented to determine when these methods behave like standard random forests, guiding a lightweight tuning approach. Experiments on public classification benchmarks show DM and DW consistently match or outperform other random‑forest baselines with minimal extra runtime.
By Quoc Viet Le, Joonha Park
arXiv:2607. 21003v1 Announce Type: new Abstract: Ordinal Classification (OC) deals with classification tasks where the classes follow a natural order.
By Rafael Ayll\'on-Gavil\'an, Francisco Jos\'e Mart\'inez-Estudillo, David Guijo-Rubio, C\'esar Herv\'as-Mart\'inez, Pedro A. Guti\'errez
arXiv:2608. 16659v1 Announce Type: cross Abstract: Ensembles of decision trees are well-established methods for data stream classification.
By Daniel Nowak Assis, Jean Paul Barddal, Fabr\'icio Enembreck
arXiv:2405. 15768v2 Announce Type: replace-cross Abstract: In this paper, we address the classification of instances represented by distributions on a vector space rather than single points.
By Jia Li, Lin Lin
arXiv:2101. 05993v2 Announce Type: replace-cross Abstract: Selecting an appropriate classification algorithm for a given data set remains a challenging problem in data mining and machine learning.
By Guangtao Wang, Qinbao Song, Xiaoyan Zhu, Jiao Liu
The paper proposes a tensor‑optimization‑powered ensemble method that uses confidence tensors to capture how each weak base classifier performs across different classes. By integrating these tensors and a smooth, partially convex objective that emphasizes margin, the method improves both classification accuracy and generalization while requiring fewer base learners. The authors also prove a property of the loss gradient that enables efficient gradient‑based optimization of the constrained problem.
By Jinghui Yuan, Weijin Jiang, Zhe Cao, Fangyuan Xie, Rong Wang, Feiping Nie, Yuan Yuan