arXiv research

Improves tree model performance by considering future node splits.

IMPaCT improves node classification in chronological split temporal graphs.

In this work, we propose a novel node splitting method for regression trees and incorporate it into the regression forest framework. Unlike traditional binary splitting, where the splitting rule is selected from a predefined set of binary splitting rules via trial-and-error, the proposed node splitting method first fin…

ES-MLP combines Graph-MLP with edge splitting for node classification on both homophilic and heterophilic graphs.

Novel approach for creating interpretable classifiers using bilevel optimization of split-rules in NLDTs.

Paper proposes a novel SVM method for creating survival trees.

DiPriMe forests use private medians to create balanced tree splits for privacy-protected data.

FONDUE identifies ambiguous nodes in networks for better analysis.

SBAMDT uses adaptive soft splits to model complex decision boundaries.

We investigate how asymmetrizing an impurity function affects the choice of optimal node splits when growing a decision tree for binary classification. In particular, we relax the usual axioms of an impurity function and show how skewing an impurity function biases the optimal splits to isolate points of a particular c…

A new method identifies class-specific covariates in multi-class prediction tasks.

Random survival forests (RSF) are a powerful method for risk prediction of right-censored outcomes in biomedical research. RSF use the log-rank split criterion to form an ensemble of survival trees. The most common approach to evaluate the prediction accuracy of a RSF model is Harrell's concordance index for survival d…

While many statistical models and methods are now available for network analysis, resampling network data remains a challenging problem. Cross-validation is a useful general tool for model selection and parameter tuning, but is not directly applicable to networks since splitting network nodes into groups requires delet…

CDF uses centroids to split features for high-dimensional classification.

DaRE forests enable efficient data deletion from random forests.

This work introduces a transformation-based learner model for classification forests. The weak learner at each split node plays a crucial role in a classification tree. We propose to optimize the splitting objective by learning a linear transformation on subspaces using nuclear norm as the optimization criteria. The le…

Convex polytope trees expand decision trees with interpretable boundaries.

This paper presents an improvement to model learning when using multi-class LogitBoost for classification. Motivated by the statistical view, LogitBoost can be seen as additive tree regression. Two important factors in this setting are: 1) coupled classifier output due to a sum-to-zero constraint, and 2) the dense Hess…

Decision trees with binary splits are popularly constructed using Classification and Regression Trees (CART) methodology. For binary classification and regression models, this approach recursively divides the data into two near-homogenous daughter nodes according to a split point that maximizes the reduction in sum of …

USNRT uses tree-structured learning to improve uncertainty quantification of variance networks.

Tree-based algorithm for functional data analysis reduces generalization error.

The paper studies statistical properties of CART regression trees.

We consider multi-label classification where the goal is to annotate each data point with the most relevant $\textit{subset}$ of labels from an extremely large label set. Efficient annotation can be achieved with balanced tree predictors, i.e. trees with logarithmic-depth in the label complexity, whose leaves correspon…

Simultaneous Latent Budget Trees for stratified classification

Unified representation for tree ensembles indexed by nodes

Improved sampling for network community detection.

Neural Networks and Decision Trees: two popular techniques for supervised learning that are seemingly disconnected in their formulation and optimization method, have recently been combined in a single construct. The connection pivots on assembling an artificial Neural Network with nodes that allow for a gate-like funct…

New algorithm reduces discrimination in predictions.

VFGNN tackles privacy-preserving node classification with federated GNN.

CIT and CIF improve feature selection for downstream prediction.

We propose and analyze a generic method for community recovery in stochastic block models and degree corrected block models. This approach can exactly recover the hidden communities with high probability when the expected node degrees are of order $\log n$ or higher. Starting from a roughly correct community partition …

Causal trees struggle with accuracy in estimating treatment effects.

Conventional decision trees have a number of favorable properties, including interpretability, a small computational footprint and the ability to learn from little training data. However, they lack a key quality that has helped fuel the deep learning revolution: that of being end-to-end trainable, and to learn from scr…

Generalized linear model with $L_1$ and $L_2$ regularization is a widely used technique for solving classification, class probability estimation and regression problems. With the numbers of both features and examples growing rapidly in the fields like text mining and clickstream data analysis parallelization and the us…

This work evaluates graph models' robustness to structural distributional shifts.

We propose an algorithm for the non-negative factorization of an occurrence tensor built from heterogeneous networks. We use l0 norm to model sparse errors over discrete values (occurrences), and use decomposed factors to model the embedded groups of nodes. An efficient splitting method is developed to optimize the non…

Decision trees are a popular technique in statistical data classification. They recursively partition the feature space into disjoint sub-regions until each sub-region becomes homogeneous with respect to a particular class. The basic Classification and Regression Tree (CART) algorithm partitions the feature space using…

New algorithms for hierarchical classification using conformal prediction.

Max-Cut decision tree improves classification accuracy and reduces computation time.

HRF enhances tree diversity in random forests to improve performance.

Decision forests learn to model text by evaluating categorical-set conditions.

Enhances Random Forest for imbalanced functional data classification.

Graph embedding aims at learning a vector-based representation of vertices that incorporates the structure of the graph. This representation then enables inference of graph properties. Existing graph embedding techniques, however, do not scale well to large graphs. We therefore propose a framework for parallel computat…

Recently proposed budding tree is a decision tree algorithm in which every node is part internal node and part leaf. This allows representing every decision tree in a continuous parameter space, and therefore a budding tree can be jointly trained with backpropagation, like a neural network. Even though this continuity …

Semi-supervised node classification in graphs is a fundamental problem in graph mining, and the recently proposed graph neural networks (GNNs) have achieved unparalleled results on this task. Due to their massive success, GNNs have attracted a lot of attention, and many novel architectures have been put forward. In thi…

Random forests have become an important tool for improving accuracy in regression and classification problems since their inception by Leo Breiman in 2001. In this paper, we revisit a historically important random forest model originally proposed by Breiman in 2004 and later studied by Gérard Biau in 2012, where a feat…

A new tree model, GRST, improves option pricing without log-normality assumptions.

This paper explains CART random forests using stochastic control theory.