arXiv:2606. 29053v1 Announce Type: new Abstract: In general, an ensemble classifier is more accurate than a single classifier.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
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.
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.
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...
arXiv:2502. 00168v5 Announce Type: replace-cross Abstract: Supervised dimensionality reduction maps labeled data into a low-dimensional feature space while preserving class separation.
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.