Tootfinder

Spectrum Estimation through Kirchhoff Random Forests
Simon Barthelm\'e, Fabienne Castell, Alexandre Gaudilli\`ere, Clothilde M\'elot, Matteo Quattropani, Nicolas Tremblay
https://arxiv.org/abs/2507.19164

Spectrum Estimation through Kirchhoff Random Forests
Given a non-oriented edge-weighted graph, we show how to make some estimation of the associated Laplacian eigenvalues through Monte Carlo evaluation of spectral quantities computed along Kirchhoff random rooted spanning forest trajectories. The sampling cost of this estimation is only linear in the node number, up to a logarithmic factor. By associating a double cover of such a graph with any symmetric real matrix, we can then perform spectral estimation in the same way for the latter.

Estimating volumetric water content from electrical resistivity using a random forest model
Constantin Schorling
https://arxiv.org/abs/2508.16593 https://a…

Estimating volumetric water content from electrical resistivity using a random forest model
Background: Accurately estimating volumetric water content (VWC) can greatly enhance the prediction of landslide risk. The standard approach involves using locally limited, invasive sensor measurements. Recently, however, electrical resistivity tomography (ERT) has emerged as a cost-effective, minimally invasive, real-time, indirect method of monitoring VWC. However, linking ER to VWC poses distinct challenges. Purpose: Random forest models were developed to estimate and predict VWC from ER. Sp…

Crosslisted article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/2]:
- Random forest-based out-of-distribution detection for robust lung cancer segmentation
Aneesh Rangnekar, Harini Veeraraghavan

Confidence Intervals for Random Forest Permutation Importance with Missing Data
Nico F\"oge, Markus Pauly
https://arxiv.org/abs/2507.13918 https://

Confidence Intervals for Random Forest Permutation Importance with Missing Data
Random Forests are renowned for their predictive accuracy, but valid inference, particularly about permutation-based feature importances, remains challenging. Existing methods, such as the confidence intervals (CIs) from Ishwaran et al. (2019), are promising but assume complete feature observation. However, real-world data often contains missing values. In this paper, we investigate how common imputation techniques affect the validity of Random Forest permutation-importance CIs when data are in…

Missing value imputation with adversarial random forests -- MissARF
Pegah Golchian, Jan Kapar, David S. Watson, Marvin N. Wright
https://arxiv.org/abs/2507.15681

Missing value imputation with adversarial random forests -- MissARF
Handling missing values is a common challenge in biostatistical analyses, typically addressed by imputation methods. We propose a novel, fast, and easy-to-use imputation method called missing value imputation with adversarial random forests (MissARF), based on generative machine learning, that provides both single and multiple imputation. MissARF employs adversarial random forest (ARF) for density estimation and data synthesis. To impute a missing value of an observation, we condition on the no…

Binary Tree Option Pricing Under Market Microstructure Effects: A Random Forest Approach
Akash Deep, Chris Monico, W. Brent Lindquist, Svetlozar T. Rachev, Frank J. Fabozzi
https://arxiv.org/abs/2507.16701

Binary Tree Option Pricing Under Market Microstructure Effects: A Random Forest Approach
We propose a machine learning-based extension of the classical binomial option pricing model that incorporates key market microstructure effects. Traditional models assume frictionless markets, overlooking empirical features such as bid-ask spreads, discrete price movements, and serial return correlations. Our framework augments the binomial tree with path-dependent transition probabilities estimated via Random Forest classifiers trained on high-frequency market data. This approach preserves no…

Random walk reflected off of infinity, with applications to uniform spanning forests and supercritical Liouville quantum gravity
Ewain Gwynne, Jinwoo Sung
https://arxiv.org/abs/2506.18827

Random walk reflected off of infinity, with applications to uniform spanning forests and supercritical Liouville quantum gravity
Let $\mathcal G$ be an infinite graph -- not necessarily one-ended -- on which the simple random walk is transient. We define a variant of the continuous-time random walk on $\mathcal G$ which reaches $\infty$ in finite time and "reflects off of $\infty$" infinitely many times. We show that the Aldous-Broder algorithm for the random walk reflected off of $\infty$ gives the free uniform spanning forest (FUSF) on $\mathcal G$. Furthermore, Wilson's algorithm for the random walk reflected off of…

Persistent Patterns in Eye Movements: A Topological Approach to Emotion Recognition
Arsha Niksa, Hooman Zare, Ali Shahrabi, Hanieh Hatami, Mohammadreza Razvan
https://arxiv.org/abs/2507.17450

Persistent Patterns in Eye Movements: A Topological Approach to Emotion Recognition
We present a topological pipeline for automated multiclass emotion recognition from eye-tracking data. Delay embeddings of gaze trajectories are analyzed using persistent homology. From the resulting persistence diagrams, we extract shape-based features such as mean persistence, maximum persistence, and entropy. A random forest classifier trained on these features achieves up to $75.6\%$ accuracy on four emotion classes, which are the quadrants the Circumplex Model of Affect. The results demons…

Splittable Spanning Trees and Balanced Forests in Dense Random Graphs
David Gillman, Jacob Platnick, Dana Randall
https://arxiv.org/abs/2507.12707 https://…

Splittable Spanning Trees and Balanced Forests in Dense Random Graphs
Weighted equitable partitioning of a graph has been of interest lately due to several applications, including redistricting, network algorithms, and image decomposition. Weighting a partition according to the spanning-tree metric has been of mathematical and practical interest because it typically favors partitions with more compact pieces. An appealing algorithm suggested by Charikar et al. is to sample a random spanning tree and remove k-1 edges, producing a random forest. If the components o…

A new paper projecting Joshua tree habitat under future climate based on incredibly high-resolution distribution data, from Joshua Tree Genome Project collaborators at USGS. They estimate up to 80% loss of suitable habitat by 2100 under the worst-case climate scenario.
#JoshuaTree #science

Semi-supervised classification of Stars, Galaxies and Quasars using K-means and Random Forest
Vahid Asadi, Hosein Haghi, Akram Hasani Zonoozi
https://arxiv.org/abs/2507.14072

Semi-supervised classification of Stars, Galaxies and Quasars using K-means and Random Forest
Classifying stars, galaxies, and quasars is essential for understanding cosmic structure and evolution, but the vast data from modern surveys make manual classification impractical, while supervised learning methods remain constrained by the scarcity of labeled spectroscopic data. We aim to develop a scalable, label-efficient method for astronomical classification by leveraging semi-supervised learning (SSL) to overcome the limitations of fully supervised approaches. We propose a novel SSL fram…

Evaluating Ensemble and Deep Learning Models for Static Malware Detection with Dimensionality Reduction Using the EMBER Dataset
Md Min-Ha-Zul Abedin, Tazqia Mehrub
https://arxiv.org/abs/2507.16952

Evaluating Ensemble and Deep Learning Models for Static Malware Detection with Dimensionality Reduction Using the EMBER Dataset
This study investigates the effectiveness of several machine learning algorithms for static malware detection using the EMBER dataset, which contains feature representations of Portable Executable (PE) files. We evaluate eight classification models: LightGBM, XGBoost, CatBoost, Random Forest, Extra Trees, HistGradientBoosting, k-Nearest Neighbors (KNN), and TabNet, under three preprocessing settings: original feature space, Principal Component Analysis (PCA), and Linear Discriminant Analysis (L…

A U-Statistic-based random forest approach for genetic interaction study
Ming Li, Ruo-Sin Peng, Changshuai Wei, Qing Lu
https://arxiv.org/abs/2508.14924 https://

A U-Statistic-based random forest approach for genetic interaction study
Variations in complex traits are influenced by multiple genetic variants, environmental risk factors, and their interactions. Though substantial progress has been made in identifying single genetic variants associated with complex traits, detecting the gene-gene and gene-environment interactions remains a great challenge. When a large number of genetic variants and environmental risk factors are involved, searching for interactions is limited to pair-wise interactions due to the exponentially i…

2/2 I continued blogging Alberniweather and on FB and Twitter but I gradually removed my personal self from Facebook and eventually during the Pandemic, I decided the Facebook environment was just too toxic even for weather stuff and I shut down my page and left Facebook completely.
The impact on traffic to Alberniweather.ca and its prominence in the community was, and still is, significant.
I have diehard followers, many who have become friends over the years, I still get the odd call from media, or even the public about random weather things.
I have good connections with a few folks at Environment Canada (though their staff have become thinner and more transient :(
and major events still get spikes of local traffic but I since about 2022, and after I removed myself from Twitter that year, I don’t blog nearly as much. I would do a few posts in a week, and then go months without posting. I just got out of the habit I guess.
But I am still interested in the weather. I still feel like Alberniweather is a useful service for people in my community. I still feel a willing obligation to inform people about the weather and I believe I am trusted to do so by the public and local leaders. I’ve never made any money at it, I sold ad space on the website for a few years but it wasn’t worth the hassle and I didn’t feel comfortable taking the money when I was councillor. I have had some generous spontaneous donations at times.
But mainly I do it because it’s interesting, and I hope it is useful for people especially when people are looking for information during a major event.
The highest traffic I have ever had on Alberniweather pre-FB exit was the local Dog Mountain forest fire in 2015.
post-FB exit: the #underwoodfire
People want easy access to reliable local, trusted, information.
Large media orgs have mostly given up on this.
I am grateful we still have an active local newspaper and radio and that both trust me and I trust them.
@… @…

CPC-CMS: Cognitive Pairwise Comparison Classification Model Selection Framework for Document-level Sentiment Analysis
Jianfei Li, Kevin Kam Fung Yuen
https://arxiv.org/abs/2507.14022

CPC-CMS: Cognitive Pairwise Comparison Classification Model Selection Framework for Document-level Sentiment Analysis
This study proposes the Cognitive Pairwise Comparison Classification Model Selection (CPC-CMS) framework for document-level sentiment analysis. The CPC, based on expert knowledge judgment, is used to calculate the weights of evaluation criteria, including accuracy, precision, recall, F1-score, specificity, Matthews Correlation Coefficient (MCC), Cohen's Kappa (Kappa), and efficiency. Naive Bayes, Linear Support Vector Classification (LSVC), Random Forest, Logistic Regression, Extreme Gradient B…

Generalized random forest for extreme quantile regression
Lucien M. Vidagbandji, Alexandre Berred, Cyrille Bertelle, Laurent Amanton
https://arxiv.org/abs/2508.15095 https://

Generalized random forest for extreme quantile regression
Quantile regression is a statistical method which, unlike classical regression, aims to predict the conditional quantiles. Classical quantile regression methods face difficulties, particularly when the quantile under consideration is extreme, due to the limited number of data available in the tail of the distribution, or when the quantile function is complex. We propose an extreme quantile regression method based on extreme value theory and statistical learning to overcome these difficulties. F…

Empirical Models of the Time Evolution of SPX Option Prices
Alessio Brini, David A. Hsieh, Patrick Kuiper, Sean Moushegian, David Ye
https://arxiv.org/abs/2506.17511

Empirical Models of the Time Evolution of SPX Option Prices
The key objective of this paper is to develop an empirical model for pricing SPX options that can be simulated over future paths of the SPX. To accomplish this, we formulate and rigorously evaluate several statistical models, including neural network, random forest, and linear regression. These models use the observed characteristics of the options as inputs -- their price, moneyness and time-to-maturity, as well as a small set of external inputs, such as the SPX and its past history, dividend …

Enhanced Predictive Modeling for Hazardous Near-Earth Object Detection: A Comparative Analysis of Advanced Resampling Strategies and Machine Learning Algorithms in Planetary Risk Assessment
Sunkalp Chandra
https://arxiv.org/abs/2508.15106

Enhanced Predictive Modeling for Hazardous Near-Earth Object Detection: A Comparative Analysis of Advanced Resampling Strategies and Machine Learning Algorithms in Planetary Risk Assessment
This study evaluates the performance of several machine learning models for predicting hazardous near-Earth objects (NEOs) through a binary classification framework, including data scaling, power transformation, and cross-validation. Six classifiers were compared, namely Random Forest Classifier (RFC), Gradient Boosting Classifier (GBC), Support Vector Classifier (SVC), Linear Discriminant Analysis (LDA), Logistic Regression (LR), and K-Nearest Neighbors (KNN). RFC and GBC performed the best, b…

Reliable Noninvasive Glucose Sensing via CNN-Based Spectroscopy
El Arbi Belfarsi, Henry Flores, Maria Valero
https://arxiv.org/abs/2506.13819 https://

Reliable Noninvasive Glucose Sensing via CNN-Based Spectroscopy
In this study, we present a dual-modal AI framework based on short-wave infrared (SWIR) spectroscopy. The first modality employs a multi-wavelength SWIR imaging system coupled with convolutional neural networks (CNNs) to capture spatial features linked to glucose absorption. The second modality uses a compact photodiode voltage sensor and machine learning regressors (e.g., random forest) on normalized optical signals. Both approaches were evaluated on synthetic blood phantoms and skin-mimicking…

TRUST: Transparent, Robust and Ultra-Sparse Trees
Albert Dorador
https://arxiv.org/abs/2506.15791 https://arxiv.org/pdf/2506.15791

TRUST: Transparent, Robust and Ultra-Sparse Trees
Piecewise-constant regression trees remain popular for their interpretability, yet often lag behind black-box models like Random Forest in predictive accuracy. In this work, we introduce TRUST (Transparent, Robust, and Ultra-Sparse Trees), a novel regression tree model that combines the accuracy of Random Forests with the interpretability of shallow decision trees and sparse linear models. TRUST further enhances transparency by leveraging Large Language Models to generate tailored, user-friendl…

To see the forest for the trees: On the infinite divisibility of unlabeled forests
Michal Bassan, Serte Donderwinkel, Brett Kolesnik
https://arxiv.org/abs/2507.16650

To see the forest for the trees: On the infinite divisibility of unlabeled forests
Inspired by Stufler's recent probabilistic proof of Otter's asymptotic number of unlabeled trees, we revisit work of Palmer and Schwenk, and study unlabeled forests from a probabilistic point of view. We show that the number of trees in a random forest converges, with all of its moments, to a shifted compound Poisson. We also find the asymptotic proportion of forests that are trees. The key fact is that the number of trees $t_n$ and forests $f_n$ are related by a Lévy process. As such, the res…

Enhancing Urban GNSS Positioning Reliability via Conservative Satellite Selection Using Unanimous Voting Across Multiple Machine Learning Classifiers
Sanghyun Kim, Jiwon Seo
https://arxiv.org/abs/2507.12706

Enhancing Urban GNSS Positioning Reliability via Conservative Satellite Selection Using Unanimous Voting Across Multiple Machine Learning Classifiers
In urban environments, global navigation satellite system (GNSS) positioning is often compromised by signal blockages and multipath effects caused by buildings, leading to significant positioning errors. To address this issue, this study proposes a robust enhancement of zonotope shadow matching (ZSM)-based positioning by employing a conservative satellite selection strategy using unanimous voting across multiple machine learning classifiers. Three distinct models - random forest (RF), gradient …

Trustworthy Tree-based Machine Learning by $MoS_2$ Flash-based Analog CAM with Inherent Soft Boundaries
Bo Wen, Guoyun Gao, Zhicheng Xu, Ruibin Mao, Xiaojuan Qi, X. Sharon Hu, Xunzhao Yin, Can Li
https://arxiv.org/abs/2507.12384

Trustworthy Tree-based Machine Learning by $MoS_2$ Flash-based Analog CAM with Inherent Soft Boundaries
The rapid advancement of artificial intelligence has raised concerns regarding its trustworthiness, especially in terms of interpretability and robustness. Tree-based models like Random Forest and XGBoost excel in interpretability and accuracy for tabular data, but scaling them remains computationally expensive due to poor data locality and high data dependence. Previous efforts to accelerate these models with analog content addressable memory (CAM) have struggled, due to the fact that the diff…

On feature selection in double-imbalanced data settings: a Random Forest approach
Fabio Demaria
https://arxiv.org/abs/2506.10929 https://

On feature selection in double-imbalanced data settings: a Random Forest approach
Feature selection is a critical step in high-dimensional classification tasks, particularly under challenging conditions of double imbalance, namely settings characterized by both class imbalance in the response variable and dimensional asymmetry in the data $(n \gg p)$. In such scenarios, traditional feature selection methods applied to Random Forests (RF) often yield unstable or misleading importance rankings. This paper proposes a novel thresholding scheme for feature selection based on mini…

The Directed Spanning Forest: coalescence versus dimension
Tom Garcia-Sanchez
https://arxiv.org/abs/2507.13289 https://arxiv.org/pdf/…

The Directed Spanning Forest: coalescence versus dimension
For $p\in[1,\infty]$, the $\ell^p$ directed spanning forest (DSF) of dimension $d\geq 2$ is an oriented random geometric graph whose vertex set is given by a homogeneous Poisson point process $\mathcal N$ on $\mathbb R^d$ and whose edges consist of all pairs $(x, y)\in\mathcal N^2$ such that $y$ is the closest point to $x$ in $\mathcal N$ for the $\ell^p$ distance among points with a strictly larger $e_d$ coordinate. First introduced by Baccelli and Bordenave in 2007 in the case $p=d=2$, this g…

Asymptotic Consistency and Generalization in Hybrid Models of Regularized Selection and Nonlinear Learning
Luciano Ribeiro Galv\~ao, Rafael de Andrade Mora
https://arxiv.org/abs/2508.07754

Asymptotic Consistency and Generalization in Hybrid Models of Regularized Selection and Nonlinear Learning
This study explores how different types of supervised models perform in the task of predicting and selecting relevant variables in high-dimensional contexts, especially when the data is very noisy. We analyzed three approaches: regularized models (such as Lasso, Ridge, and Elastic Net), black-box models (such as Random Forest, XGBoost, LightGBM, CatBoost, and H2O GBM), and hybrid models that combine both approaches: regularization with nonlinear algorithms. Based on simulations inspired by the …

FedCVD : Communication-Efficient Federated Learning for Cardiovascular Risk Prediction with Parametric and Non-Parametric Model Optimization
Abdelrhman Gaber, Hassan Abd-Eltawab, John Elgallab, Youssif Abuzied, Dineo Mpanya, Turgay Celik, Swarun Kumar, Tamer ElBatt
https://arxiv.org/abs/2507.22963…

FedCVD++: Communication-Efficient Federated Learning for Cardiovascular Risk Prediction with Parametric and Non-Parametric Model Optimization
Cardiovascular diseases (CVD) cause over 17 million deaths annually worldwide, highlighting the urgent need for privacy-preserving predictive systems. We introduce FedCVD++, an enhanced federated learning (FL) framework that integrates both parametric models (logistic regression, SVM, neural networks) and non-parametric models (Random Forest, XGBoost) for coronary heart disease risk prediction. To address key FL challenges, we propose: (1) tree-subset sampling that reduces Random Forest communi…

INLA-RF: A Hybrid Modeling Strategy for Spatio-Temporal Environmental Data
Mario Figueira, Michela Cameletti, Luca Patelli
https://arxiv.org/abs/2507.18488 https://

INLA-RF: A Hybrid Modeling Strategy for Spatio-Temporal Environmental Data
Environmental processes often exhibit complex, non-linear patterns and discontinuities across space and time, posing significant challenges for traditional geostatistical modeling approaches. In this paper, we propose a hybrid spatio-temporal modeling framework that combines the interpretability and uncertainty quantification of Bayesian models -- estimated using the INLA-SPDE approach -- with the predictive power and flexibility of Random Forest (RF). Specifically, we introduce two novel algor…

Benchmarking Classical and Quantum Models for DeFi Yield Prediction on Curve Finance
Chi-Sheng Chen, Aidan Hung-Wen Tsai
https://arxiv.org/abs/2508.02685 https://

Benchmarking Classical and Quantum Models for DeFi Yield Prediction on Curve Finance
The rise of decentralized finance (DeFi) has created a growing demand for accurate yield and performance forecasting to guide liquidity allocation strategies. In this study, we benchmark six models, XGBoost, Random Forest, LSTM, Transformer, quantum neural networks (QNN), and quantum support vector machines with quantum feature maps (QSVM-QNN), on one year of historical data from 28 Curve Finance pools. We evaluate model performance on test MAE, RMSE, and directional accuracy. Our results show …

Machine Learning for Exoplanet Detection: A Comparative Analysis Using Kepler Data
Reihaneh Karimi, Mahdiyar Mousavi-Sadr, Mohammad H. Zhoolideh Haghighi, Fatemeh S. Tabatabaei
https://arxiv.org/abs/2508.09689

Machine Learning for Exoplanet Detection: A Comparative Analysis Using Kepler Data
The discovery of exoplanets has expanded our understanding of planetary systems and opened new avenues for astronomical research. In this study, we present a machine learning (ML) framework for exoplanet identification using a time-series photometric dataset from the Kepler Space Telescope, comprising 3,198 flux measurements across 5,074 stars. We investigate the performance of four supervised classification algorithms, namely Random Forest, k-Nearest Neighbors (KNN), Decision Tree, and Logisti…

Crosslisted article(s) found for stat.ME. https://arxiv.org/list/stat.ME/new
[1/1]:
- A U-Statistic-based random forest approach for genetic interaction study
Ming Li, Ruo-Sin Peng, Changshuai Wei, Qing Lu

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/5]:
- Guided Random Forest and its application to data approximation
Prashant Gupta, Aashi Jindal, Jayadeva, Debarka Sengupta

Star formation drivers across the M33 disk
Edvige Corbelli, Bruce Elmegreen, Sara Ellison, Simone Bianchi
https://arxiv.org/abs/2507.01158 https://

Star formation drivers across the M33 disk
We investigate the star formation process across the disk of M33 using a multiwavelength dataset and disk dynamics. We computed numerically equilibrium values of gas densities and scale heights across the disk, taking into account dark matter and testing several analytic approximations that are often used to estimate these variables and the hydrostatic pressure. Orthogonal regressions and hierarchical Bayesian models, as well as random forest (RF) analyses, were used to establish the fundamenta…

This https://arxiv.org/abs/2412.07184 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Automatic Doubly Robust Forests
This paper proposes the automatic Doubly Robust Random Forest (DRRF) algorithm for estimating the conditional expectation of a moment functional in the presence of high-dimensional nuisance functions. DRRF extends the automatic debiasing framework based on the Riesz representer to the conditional setting and enables nonparametric, forest-based estimation (Athey et al., 2019; Oprescu et al., 2019). In contrast to existing methods, DRRF does not require prior knowledge of the form of the debiasin…

Uncertainty-Aware Genomic Classification of Alzheimer's Disease: A Transformer-Based Ensemble Approach with Monte Carlo Dropout
Taeho Jo, Eun Hye Lee, Alzheimer's Disease Sequencing Project
https://arxiv.org/abs/2506.00662

Uncertainty-Aware Genomic Classification of Alzheimer's Disease: A Transformer-Based Ensemble Approach with Monte Carlo Dropout
INTRODUCTION: Alzheimer's disease (AD) is genetically complex, complicating robust classification from genomic data. METHODS: We developed a transformer-based ensemble model (TrUE-Net) using Monte Carlo Dropout for uncertainty estimation in AD classification from whole-genome sequencing (WGS). We combined a transformer that preserves single-nucleotide polymorphism (SNP) sequence structure with a concurrent random forest using flattened genotypes. An uncertainty threshold separated samples into …

A novel two-stage parameter estimation framework integrating Approximate Bayesian Computation and Machine Learning: The ABC-RF-rejection algorithm
Renata Retkute, Christopher A. Gilligan
https://arxiv.org/abs/2507.02072

A novel two-stage parameter estimation framework integrating Approximate Bayesian Computation and Machine Learning: The ABC-RF-rejection algorithm
We introduce a novel two-stage parameter estimation framework designed to improve computational efficiency in settings involving complex, stochastic, or analytically intractable dynamic models. The proposed method, termed \textit{ABC-RF-rejection}, integrates Approximate Bayesian Computation (ABC) rejection sampling with Random Forest (RF) classification to efficiently screen parameter sets that produce simulations consistent with observed data. We evaluate the performance of this approach usin…