arXiv Machine Learning
Jun 30
A Kernel Fisher Discriminant Analysis-Based Tree Ensemble Classifier: KFDA Forest
arXiv:2606. 29053v1 Announce Type: new Abstract: In general, an ensemble classifier is more accurate than a single classifier.
Gradient boosting with vector-valued leafs
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.
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arXiv Machine Learning
Jul 16
Parallel gradient boosting for flexible estimation of conditional distributions
arXiv:2607. 13550v1 Announce Type: cross Abstract: Boosting is one of the most successful learning techniques for standard classification and regression tasks.
arXiv Machine Learning
Sep 10
Sparse Oblique Rule Boosting for Simpler Additive Rule Ensembles
arXiv:2609.06426v1 Announce Type: new Abstract: Small additive ensembles of symbolic rules offer interpretable prediction models. Traditionally, these ensembles use rule conditions based on conjuncti...
Hugging Face Trending Papers
Jul 15
Parallel gradient boosting for flexible estimation of conditional distributions
Boosting is one of the most successful learning techniques for standard classification and regression tasks. Its extension to multi-output prediction problems has found an increasing number of applications in recent years.
arXiv Machine Learning
Jun 24
Experiments with Optimal Model Trees
arXiv:2503. 12902v4 Announce Type: replace Abstract: Model trees provide an appealing way to perform interpretable machine learning for both classification and regression problems.
arXiv Machine Learning
Jul 17
Analytical study of the optimal combination of binary classifiers based on classifiers-induced partitioning of the training set
arXiv:2607. 14889v1 Announce Type: new Abstract: This paper studies an optimal linear combination of binary classifiers based on a logical structuration of the dataset via truth tables.
arXiv Machine Learning
Jun 15
A General Framework for Decision Trees via Bregman Divergences
arXiv:2606. 13984v1 Announce Type: cross Abstract: Decision trees are one of the fundamental tools in statistical learning due to their interpretability, flexibility, and their ability to adapt to nonlinear structures.
Hugging Face Trending Papers
Aug 20
DICS: Data-Informed Centroid Splitting for Decision Tree Classifiers
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 Machine Learning
Jul 31
Search Strategies for Optimal Classification and Regression Trees
arXiv:2607. 28170v1 Announce Type: new Abstract: Optimal decision trees (ODTs) are compact, interpretable machine learning models that globally optimize a given objective, but their scalability remains challenging.
arXiv Machine Learning
Aug 21
DICS: Data-Informed Centroid Splitting for Decision Tree Classifiers
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.
arXiv Machine Learning
Aug 26
qshap: Fast Shapley Decomposition of $R^2$ for Gradient-Boosted Trees
arXiv:2608.24104v1 Announce Type: cross Abstract: Numerous methods have been developed to quantify feature attributions in individual predictions for tree ensembles. However, many applications requir...
arXiv Machine Learning
Jun 5
Quantifying Sensitivity for Tree Ensembles: A symbolic and compositional approach
arXiv:2605. 13830v2 Announce Type: replace-cross Abstract: Decision tree ensembles (DTE) are a popular model for a wide range of AI classification tasks, used in multiple safety critical domains, and hence verifying properties on these models has been an active topic of study over the last decade.