arXiv research

Detecting correlated trees helps align sparse graphs.

DBDT uses deep boosting decision trees for fraud detection.

Study evaluates Tree-Ring Watermarking in rectified flow-based models, revealing detection and separability limitations.

Paper describes anomaly detection and explainability for multivariate functional data.

Paper detects common subtrees with identical labels in trees.

OCmst detects anomalies using CNN features and MSTs.

Two new scoring methods improve anomaly detection in Isolation Forest.

Improved isolation forest for better outlier detection.

DBT combines diffusion models and boosting for supervised learning.

GLMM trees identify subgroups with different growth patterns in longitudinal data.

We consider the detection of activations over graphs under Gaussian noise, where signals are piece-wise constant over the graph. Despite the wide applicability of such a detection algorithm, there has been little success in the development of computationally feasible methods with proveable theoretical guarantees for ge…

Resource-efficient oblique trees reduce neural signal classification costs.

DiFF-RF detects point-wise and collective anomalies using random partitioning trees.

FairUDT uses uplift decision trees to detect and mitigate discrimination in training data.

The paper explores statistical limits for detecting correlation in tree structures.

Bayesian learning for forests and trees improves graph detection and structure learning.

DTOR explains anomalies with rule-based explanations.

We explore the use of random forest and gradient boosting, two powerful tree-based machine learning algorithms, for the detection of cosmic strings in maps of the cosmic microwave background (CMB), through their unique Gott-Kaiser-Stebbins effect on the temperature anisotropies.The information in the maps is compressed…

Bayesian Context Trees improve change-point detection in discrete data.

Enhanced anomaly detection using PRC-RF with autoencoders.

The use of autonomous vehicles (AVs) is a promising technology in Intelligent Transportation Systems (ITSs) to improve safety and driving efficiency. Vehicle-to-everything (V2X) technology enables communication among vehicles and other infrastructures. However, AVs and Internet of Vehicles (IoV) are vulnerable to diffe…

We present a new package in R implementing Bayesian additive regression trees (BART). The package introduces many new features for data analysis using BART such as variable selection, interaction detection, model diagnostic plots, incorporation of missing data and the ability to save trees for future prediction. It is …

We consider the problem of change-point detection in multivariate time-series. The multivariate distribution of the observations is supposed to follow a graphical model, whose graph and parameters are affected by abrupt changes throughout time. We demonstrate that it is possible to perform exact Bayesian inference when…

We utilize Wi-Fi communications from smartphones to predict their mobility mode, i.e. walking, biking and driving. Wi-Fi sensors were deployed at four strategic locations in a closed loop on streets in downtown Toronto. Deep neural network (Multilayer Perceptron) along with three decision tree based classifiers (Decisi…

Paper detects bias in AI medical models using CART.

Phase Modulation on the Hypersphere (PMH) is a power efficient modulation scheme for the \textit{load-modulated} multiple-input multiple-output (MIMO) transmitters with central power amplifiers (CPA). However, it is difficult to obtain the precise channel state information (CSI), and the traditional optimal maximum lik…

This work describes an outlier detection procedure (named "OutlierTree") loosely based on the GritBot software developed by RuleQuest research, which works by evaluating and following supervised decision tree splits on variables, in whose branches 1-d confidence intervals are constructed for the target variable and pot…

The problem of categorical data analysis in high dimensions is considered. A discussion of the fundamental difficulties of probability modeling is provided, and a solution to the derivation of high dimensional probability distributions based on Bayesian learning of clique tree decomposition is presented. The main contr…

Hierarchical latent tree analysis (HLTA) is recently proposed as a new method for topic detection. It differs fundamentally from the LDA-based methods in terms of topic definition, topic-document relationship, and learning method. It has been shown to discover significantly more coherent topics and better topic hierarc…

ABIForest improves anomaly detection using attention weights.

Study examines unsupervised and graph-based methods for anomaly detection in IoBT, outperformed by supervised stacking ensemble.

Graph regularized autoencoder improves anomaly detection performance.

Automates optimizer design for diverse tasks efficiently.

New method builds robust trees from noisy data.

A framework detects nonlinear and interaction effects in epidemiological data with uncertainty quantification.

Dynamic Vine Copulas detect and quantify time-varying higher-order interactions in multivariate systems.

We present a method for incorporating missing data in non-parametric statistical learning without the need for imputation. We focus on a tree-based method, Bayesian Additive Regression Trees (BART), enhanced with "Missingness Incorporated in Attributes," an approach recently proposed incorporating missingness into deci…

Deep learning detects APT attacks with high accuracy and low false positives.

Nowadays, data are generated massively and rapidly from scientific fields as bioinformatics, neuroscience and astronomy to business and engineering fields. Cluster analysis, as one of the major data analysis tools, is therefore more significant than ever. We propose in this work an effective Semi-supervised Divisive Cl…

Improved Random Forests detect pure interactions better.

We investigate hierarchical structure in various complex systems according to Minimum Spanning Tree methods. Firstly, we investigate stock markets where the graphis obtained from the matrix of correlations coefficient computed between all pairs of assets by considering the synchronous time evolution of the difference o…

PIF detects anomalies in structured patterns using preference embedding.

A new test statistic counts tree co-occurrences to detect edge correlation between networks.

Model improves fraud detection for new scenes with limited data.

Proposes using MLP for predicting optimal penalty in changepoint detection.

Quantile gradient boosted trees outperform other models in predicting NO2 concentration distributions.

We provide a systematic study of the problem of finding the source of a rumor in a network. We model rumor spreading in a network with a variant of the popular SIR model and then construct an estimator for the rumor source. This estimator is based upon a novel topological quantity which we term \textbf{rumor centrality…

iMondrian forest combines isolation forest and Mondrian forest for better anomaly detection.