Improved accuracy in machine learning with Cross-Cluster Weighted Forests.
problem Improving accuracy in machine learning algorithms for datasets with clusters.
method Ensembling Random Forest learners trained on clusters determined by k-means.
result Significant improvements in accuracy and generalizability over traditional Random Forest.
Develops a new random forest method for clustered data with improved prediction and inference.
problem Improving prediction and inference accuracy for clustered data with within-cluster dependence.
method Clustered Random Forests, using weighted least squares estimators for leaf predictions.
result Optimal prediction and inference weights vary under covariate shift, necessitating user-chosen weights.
Differentiable clustering method using perturbed spanning forests.
problem Efficient clustering in trainable pipelines with noisy data.
method Stochastic perturbations of minimum-weight spanning forests.
result Method performs well even in challenging settings.
Proposes a new clustering algorithm using random forest.
problem Density-based clustering with optimal level determination.
method Best-scored random forest algorithm.
result Guaranteed consistency and fast convergence rates.
Forest Fire Clustering discovers cell types from single-cell data.
problem Discovering cell types from large-scale single-cell sequencing data.
method Iterative label propagation and parallelized Monte Carlo simulation.
result Forest Fire Clustering outperforms state-of-the-art methods on diverse benchmarks.
With inspiration from Random Forests (RF) in the context of classification, a new clustering ensemble method---Cluster Forests (CF) is proposed. Geometrically, CF randomly probes a high-dimensional data cloud to obtain "good local clusterings" and then aggregates via spectral clustering to obtain cluster assignments fo…
Proposes rpf-kernel for clustering via random projection forests.
problem Clustering similar data points while distinguishing them from dissimilar ones.
method Random projection forests to learn a similarity kernel.
result rpf-kernel effectively clusters data with competitive performance.
LILI clustering reduces bias in causal inference by grouping similar counterfactual outcomes.
problem Bias in causal inference from causal forest methods.
method LILI clustering algorithm integrates causal trees through leaf similarity.
result LILI clustering reduces bias and improves prediction accuracy for ATE.
This paper introduces a new method to cluster qualitative attribute data using tree structures.
problem Clustering qualitative attribute data, especially when values are not in Euclidean space.
method Developed a joint learning mechanism to iteratively learn trees representing qualitative values' order relationships.
result The joint learning mechanism successfully clusters qualitative attribute data, yielding accurate results.
A method to improve clustering explainability using bagging and feature dropout.
problem Lack of explainability in clustering methods.
method Bagging and feature dropout to generate feature importance scores.
result Improved stability and robustness of cluster definition, especially in small-sample or noisy settings.
Machine learning helps predict smoke types for safer forest burns.
problem Determining which fuels to burn safely to reduce wildfire risk and minimize smoke.
method Machine learning techniques like spectral clustering and manifold learning.
result Interpretable representations and tools for differentiating smoke types.
Paper improves anomaly detection by using non-uniform random choices in isolation forests.
problem Detecting clustered diverse outliers more effectively.
method Comparing different split guiding criteria in isolation forests.
result Non-uniform random choices improve outlier discrimination for certain outlier classes.
SLIC-UAV monitors forest recovery using UAVs and machine learning.
problem Challenges in monitoring forest recovery, especially in logged tropical forests.
method Novel pipeline for UAV imagery analysis, combining crown labelling, species classification, and superpixel segmentation.
result SLIC-UAV achieves high accuracy in species mapping, from 79.3% to 90.5%.
Categorical Co-Frequency Analysis clusters diagnoses to predict hospital readmissions.
problem Predicting patients' risk of 30-day hospital readmission.
method Categorical Co-Frequency Analysis (CoFA) measures diagnosis similarity using random forests.
result Identified three groups of diagnoses with varying readmission risk.
New methods optimize personalized treatment assignment in trials with many arms.
problem Poor performance of standard methods in trials with many treatment arms.
method Regularized and clustered joint assignment forest algorithm.
result Gains in predicting arm-wise outcomes and utility gains from personalization.
Exploratory data analysis is crucial for developing and understanding classification models from high-dimensional datasets. We explore the utility of a new unsupervised tree ensemble called uncharted forest for visualizing class associations, sample-sample associations, class heterogeneity, and uninformative classes fo…
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…
Factor analysis or sometimes referred to as variable analysis has been extensively used in classification problems for identifying specific factors that are significant to particular classes. This type of analysis has been widely used in application such as customer segmentation, medical research, network traffic, imag…
We present Random Partition Kernels, a new class of kernels derived by demonstrating a natural connection between random partitions of objects and kernels between those objects. We show how the construction can be used to create kernels from methods that would not normally be viewed as random partitions, such as Random…
Proposes autoencoding with random forests using spectral graph theory.
problem Learning low-dimensional embeddings of random forest models.
method Combines nonparametric statistics and spectral graph theory for optimization.
result Establishes a universal consistent decoder for random forest models.
Metric learning is a key problem for many data mining and machine learning applications, and has long been dominated by Mahalanobis methods. Recent advances in nonlinear metric learning have demonstrated the potential power of non-Mahalanobis distance functions, particularly tree-based functions. We propose a novel non…
Big Data is one of the major challenges of statistical science and has numerous consequences from algorithmic and theoretical viewpoints. Big Data always involve massive data but they also often include online data and data heterogeneity. Recently some statistical methods have been adapted to process Big Data, like lin…
Unsupervised clustering can reproduce categorization systems if features and metrics are correctly selected.
problem Reproducing expert-provided categorization systems using unsupervised clustering.
method Investigated using toy datasets and real-world fund categorization. Used appropriate feature selection and a supervised Random Forest-based distance metric.
result Unsupervised clustering can reproduce ground truth classes if features and metrics are correctly selected.
Researchers predict butt rot volume using harvester data and remote sensing.
problem Predicting butt rot volume in Norway spruce stands for optimal forest management.
method Used random forest models with harvester information, remote sensing, and environmental data.
result Remotely sensed predictor variables were more important than environmental variables.
New unsupervised methods for anomaly detection and clustering in structured and streaming data.
problem Anomaly detection and clustering in structured and streaming data.
method Preference Isolation Forest (PIF), Sliding-PIF, MultiLink, Online-iForest, MaxLogit.
result Methods outperform existing techniques on synthetic and real datasets.
The study uses unsupervised machine learning to identify top European football teams.
problem Selecting teams for the new European football Super League.
method Used Laplacian eigenmaps clustering on performance data.
result Successfully identified four clusters of teams based on performance metrics.
New outlier detection method using graph Laplacian spectrum boosts performance.
problem Detecting outliers in large datasets efficiently.
method Boosted outlier detection based on graph Laplacian spectrum.
result Outperforms existing methods on synthetic datasets.
Proposes a novel method to cluster individuals based on treatment effects.
problem Identifying subpopulations with different treatment responses.
method Clusters individuals using a learned kernel derived from causal forests, revealing latent subgroup structures.
result Captures meaningful treatment effect heterogeneity through kernelized clustering.
Simulation study evaluates tree-based imputation methods for multi-level data.
problem Ignoring dependencies in hierarchical data can compromise imputation accuracy.
method Chained Random Forests and Extreme Gradient Boosting (mixgb) adapted for multi-level data.
result Adapted boosting methods outperform traditional MICE for Level-1 variables at higher missingness rates.
Without access to large compute clusters, building random forests on large datasets is still a challenging problem. This is, in particular, the case if fully-grown trees are desired. We propose a simple yet effective framework that allows to efficiently construct ensembles of huge trees for hundreds of millions or even…
Improved clustering and anomaly detection using latent space conditioning.
problem Anomaly detection on unlabeled data.
method Conditional latent space variational autoencoder (cLSVAE) that separates latent space by conditioning on data labels.
result The method outperforms typical variational autoencoders in clustering and anomaly detection.
SDRF estimates complex survey designs for conditional distributions.
problem Estimating conditional distributions under complex survey designs.
method Survey-calibrated distributional random forest (SDRF) with pseudo-population bootstrap and MMD split criterion.
result Established design consistency and model consistency for survey designs.
This paper investigates the use of distributed processing on the problem of emotion recognition from physiological sensors using a popular machine learning library on distributed mode. Specifically, we run a random forests classifier on the biosignal-data, which have been pre-processed to form exclusive groups in an un…
Proposes a black-box attack to test clustering algorithms' robustness.
problem Testing robustness of clustering algorithms against adversarial noise.
method Formulates as a constrained minimization problem, uses custom AGA for optimization.
result Demonstrates sensitivity of clustering algorithms to adversarial samples.
In the monitoring of a complex electric grid, it is of paramount importance to provide operators with early warnings of anomalies detected on the network, along with a precise classification and diagnosis of the specific fault type. In this paper, we propose a novel multi-stage early warning system prototype for electr…
A new tree-based model for multivariate responses interprets piecewise linear regimes.
problem Recovering piecewise multivariate linear regimes in complex data.
method Twoblock clustering trees with coskewness-based dimension reduction.
result Recovery of piecewise linear regimes in data.
Two machine learning models detect anomalies in ER claims, saving up to 40% in improper payments.
problem Improper health insurance payments from fraud and upcoding.
method Two machine learning models: an upcoding model based on severity code distributions and a random forest model for claim sorting.
result Random forest model saved 12% to 40% in improper payments compared to a baseline approach.
iMondrian forest combines isolation forest and Mondrian forest for better anomaly detection.
problem Anomaly detection in batch and online settings.
method Hybrid of isolation forest and Mondrian forest, using depth in Mondrian forest structure.
result iMondrian forest outperforms existing methods in batch and online settings.
Ensembles of randomized decision trees, usually referred to as random forests, are widely used for classification and regression tasks in machine learning and statistics. Random forests achieve competitive predictive performance and are computationally efficient to train and test, making them excellent candidates for r…
New random forest method provides optimal rates and confidence bands.
problem Improving random forest regression rates and constructing confidence bands.
method Proposed Ehrenfest centered purely random forests achieve optimal rates; used Gaussian approximation for supremum of empirical processes.
result Explicit asymptotic uniform confidence bands constructed for both random forest types.
RFpredInterval package builds prediction intervals for random forests and boosted forests.
problem Quantifying uncertainty in random forest and boosted forest point predictions.
method 16 methods to build prediction intervals with random forests and boosted forests.
result The proposed method outperforms existing methods in building prediction intervals.
We propose random hinge forests, a simple, efficient, and novel variant of decision forests. Importantly, random hinge forests can be readily incorporated as a general component within arbitrary computation graphs that are optimized end-to-end with stochastic gradient descent or variants thereof. We derive random hinge…
Interpreting predictions from tree ensemble methods such as gradient boosting machines and random forests is important, yet feature attribution for trees is often heuristic and not individualized for each prediction. Here we show that popular feature attribution methods are inconsistent, meaning they can lower a featur…
Improved random forest proximities capture data geometry.
problem Inaccurate random forest proximities do not reflect learned data geometry.
method Introduce RF-GAP: Geometry- and Accuracy-Preserving proximities.
result RF-GAP improves geometric representation in tasks like data imputation.
Deep forests enhance expressiveness exponentially with depth, not width or tree size.
problem Understanding the role of depth, width, and tree size in deep forest performance.
method Provided upper and lower bounds on deep forest approximation complexity.
result Depth exponentially enhances deep forest expressiveness.
This paper improves forest pruning to balance accuracy and interpretability.
problem Limited interpretability of regression forests.
method Lasso-pruning and theoretical analysis of regression forests.
result Pruned regression forests can achieve equal or better accuracy than unpruned ones, with significant size reduction.
This paper is a comment on the survey paper by Biau and Scornet (2016) about random forests. We focus on the problem of quantifying the impact of each ingredient of random forests on their performance. We show that such a quantification is possible for a simple pure forest , leading to conclusions that could apply more…
Forest-guided smoothing uses random forest outputs for interpretable local smoothers.
problem Creating interpretable local smoothers from complex random forest outputs.
method Uses random forest outputs to define spatially adaptive bandwidth matrices for a linear smoother.
result Improves interpretability and applicability of random forest outputs for various analyses.