Improves time series classification with forest proximities.
problem Time series classification accuracy and efficiency.
method PF-GAP, an extension of RF-GAP proximities to proximity forests, combined with Multi-Dimensional Scaling and Local Outlier Factors.
result Forest proximities show stronger connection between misclassified points and outliers.
Proposes a more accurate random forest algorithm by selecting the best-performing trees.
problem Improving accuracy in binary classification problems.
method Best-scored random forest: selects the best-performing tree from random candidates.
result The best-scored random forest can be more accurate than the original random forest.
Paper assesses error estimates of Random Forests classification.
problem Quantitative assessment of Random Forests error estimates.
method Theoretical and empirical investigation of various error estimation methods.
result Random Forests' error estimates are closer to true error rate than average prediction error.
We improve random forest consistency and performance with DMRF, a new variant.
problem Improving the consistency and performance of random forest models.
method Developed DMRF, a data-driven multinomial random forest, by modifying proof methods and improving data utilization.
result DMRF achieves strong consistency with probability 1, surpassing previous models in classification tasks.
daForest improves deep forest ensemble for classification with better performance.
problem Improving classification accuracy with small training sets.
method SAMME.R boosting, feed-forward connection, hyper-parameters optimization.
result daForest outperforms neural networks and achieves state-of-the-art results.
Random Forests are extended to one-class classification.
problem No extension of RFs to one-class setting.
method Natural methodology to extend splitting criteria.
result Empirical demonstration of relevance of approach.
Forest tree species mapped with high accuracy using satellite data.
problem Classifying dominant tree species in Swedish forests.
method Extreme gradient boosting model with Bayesian optimization, combining Sentinel-1/2 satellite data and field observations.
result Overall accuracy of 85%, F1 score of 0.82, Matthews correlation coefficient of 0.81.
Interactive graphics help diagnose forest classifiers in R.
problem Exploring and understanding complex ensemble models like random forests.
method Constructing and visualizing data interactively using R packages.
result Interactive graphics provide insights into model complexity and variable importance.
Calibrated Boosting-Forest improves ranking and probability calibration in classification tasks.
problem Need for superior ranking power and well-calibrated probability estimates in classification tasks.
method Ensemble of gradient boosting machines supporting both continuous and binary labels.
result Calibrated Boosting-Forest achieves significant improvements in ranking and probability calibration compared to state-of-the-art models.
GrCAN combines autoencoder and neural decision forest for improved classification.
problem Combining robust random forest and deep neural network advantages for high-dimensional data.
method Gradient Boost Convolutional Autoencoder with Neural Decision Forest.
result GrCAN achieves good efficiency and prediction performance compared to baseline methods.
Online BSP-Forest improves space partitioning for large-scale classification and regression.
problem Efficient space partitioning for large-scale classification and regression problems.
method Developed an online BSP-Forest framework that expands space coverage and refines partition structure in real-time.
result Guaranteed universal consistency for both classification and regression problems.
AF improves classification models by adaptively weighting trees.
problem Improving classification model performance.
method AF combines OP2T for input-dependent weights and MIO for dynamic refinement.
result AF consistently outperforms RF, XGBoost, and other weighted RF.
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.
Adaptive weighting improves Deep Forest classifier's performance.
problem Improving Deep Forest classifier's efficiency and accuracy.
method Adaptive weighing of training instances at each cascade level, inspired by AdaBoost.
result The modified Deep Forest classifier performs better than the original.
We optimize large Random Forests into faster, smaller decision diagrams.
problem Efficiency and size of large Random Forests.
method Aggregating large Random Forests into a single, semantically equivalent decision diagram.
result Significant speed-ups and reduction in data structure size.
Random forest uses only triplet comparisons to learn from metric spaces.
problem Learning from metric spaces without direct access to data or distances.
method A novel random forest algorithm that uses only triplet comparisons.
result The proposed random forest is consistent and competitive with other methods.
We describe Information Forests, an approach to classification that generalizes Random Forests by replacing the splitting criterion of non-leaf nodes from a discriminative one -- based on the entropy of the label distribution -- to a generative one -- based on maximizing the information divergence between the class-con…
Study uses SAR data to map defoliation and regrowth in tundra-forest areas.
problem Mapping defoliation and regrowth in tundra-forest areas using SAR data.
method Novel guided nonlocal means speckle filtering of polarimetric covariance matrix.
result Over 99.7% classification accuracy in defoliation and regrowth mapping.
Simplifies random forests by breaking down trees into rules for better interpretability.
problem Balancing model complexity and accuracy in random forests.
method Breaking down random forest trees into individual classification rules and selecting a subset.
result A few selected rules can achieve acceptable accuracy similar to the original model, leading to simpler models.
PPF uses projections to improve classification accuracy.
problem Improving classification accuracy in multi-class problems.
method PPF constructs trees using projections of variables, enhancing traditional random forest.
result PPF outperforms traditional random forest in multi-class problems.
The paper studies a rebalanced dataset for imbalanced classification using Centered Random Forests.
problem Imbalanced classification where one class is underrepresented.
method Theoretical analysis of Centered Random Forests (CRF) with rebalanced datasets and debiasing techniques.
result Theoretical Central Limit Theorem (CLT) for the infinite CRF and debiased estimator IS-ICRF.
This paper improves cross-domain learning using random forests for manifold alignment.
problem Improving cross-domain learning and feature integration.
method Semi-supervised manifold alignment using random forest proximities.
result Random forest proximities enhance downstream classification accuracy.
Improved random forest models enhance machine learning predictions.
problem Equal weights for random forest base decision trees are not optimal.
method Proposes algorithms to modify weighting strategy of regular random forest.
result Numerical results show significant improvements over regular random forest.
Improved Mondrian Forests achieve optimal rate for online classification.
problem Consistency and minimax rates for online Mondrian Forests.
method Modified Mondrian Forest algorithm with increasing lifetime parameters and alternative updating rule.
result Achieves minimax rate for Lipschitz regression function estimation.
OPFython simplifies Optimum-Path Forest for Python users.
problem Lack of diversity and complexity in conventional classification algorithms.
method Develops a Python-based Optimum-Path Forest framework.
result OPFython provides a more friendly and faster prototyping environment.
Enhanced Random Forests outperform XGBoost across binary classification datasets.
problem Improving performance of Random Forests in binary classification.
method Adaptive sample and model weighting, iterative algorithm for sample weights, personalized tree weighting schemes.
result Significantly outperforms XGBoost across 15 binary classification datasets.
Study reduces dataset size by 3 attributes to 73% classification accuracy.
problem Reducing dataset size for gender classification in TSI Life Satisfaction Survey.
method Attribute selection using Chi2 algorithm, followed by classification tests with Naive Bayes and Random Forest.
result Best classification rate achieved with 3 attributes selected by Chi2 algorithm.
Random forest can be adapted for open-set recognition with improved performance.
problem Handling unknown classes in real-world classification tasks.
method Incorporating distance metric learning and distance-based open-set recognition into random forest.
result The proposed method outperforms state-of-the-art open-set recognition methods.
A new ensemble learning method called Residual Likelihood Forests improves performance and reduces model size.
problem Improving machine learning classification performance with compact models.
method Sequential optimization of conditional likelihoods in a boosting-like framework, combining multiplicatively.
result Significant performance improvements and reduced model size compared to other ensemble methods.
Random forests improve probability estimates through kernel regression.
problem Improving the principled approach to random forest probability estimation.
method Forge a connection between random forests and kernel regression, develop a proximity kernel model.
result Improves statistical footing of random forest probability estimation.
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…
Proposes a new method for localized uncertainty quantification in random forests using proximity measures.
problem Localized uncertainty quantification in random forests for improved reliability of predictions.
method Forming localized distributions of Out-Of-Bag (OOB) errors around nearby points defined by similarity measures (proximities) to create prediction intervals for regression and trust scores for classification.
result Localized prediction intervals and trust scores enhance model accuracy and provide higher accuracy-rejection AUC scores than competing methods.
SIRUS creates interpretable rules from random forests for manufacturing.
problem Lack of interpretability in complex models for critical decisions.
method SIRUS is a classification algorithm based on random forests that produces a simple list of rules.
result SIRUS achieves stability and accuracy comparable to random forests.
Random forests classify Pokemon names based on evolutionary status.
problem Classifying Pokemon names into pre-evolution and post-evolution categories.
method Trained random forests using sound symbolism from Pokemon names, conducted experiments with human participants.
result Random forests outperform human participants in classifying Pokemon names.
Enhances random forest consistency and introduces DMRF for improved performance.
problem Improving the consistency and efficiency of random forest algorithms.
method Strengthened proof methods and propose DMRF algorithm.
result DMRF achieves better theoretical and experimental performance than previous variants.
Paper presents a self-adaptive learning model for robust classification and regression.
problem Dealing with various datasets of different complexity.
method Combines DNDN and DSP, an end-to-end training approach with multiple randomly initialized softmax layers and adaptive soft pruning.
result The model demonstrates no performance loss compared with unpruned models and higher robustness over different data and feature distributions.
Improved time series classification with imputed data using label-guided forest-based methods.
problem Missing data in time series data.
method Label-guided imputation using forest-based proximity measures.
result Imputation leads to higher classification accuracies, even with imputed values differing from true values.
Enhances Random Forest for imbalanced functional data classification.
problem Challenges in classifying imbalanced functional data.
method Functional Random Forest with Adaptive Cost-Sensitive Splitting (FRF-ACS).
result Significantly improves minority class recall and predictive performance.
RFSVM uses learned RF similarity for HDLSS classification.
problem High dimension, low sample size classification problems.
method Transposes RFD approach to HDLSS classification using RF similarity as SVM kernel.
result RFSVM outperforms existing methods for HDLSS problems.
Rotation forest is superior to other classifiers for problems with continuous features.
problem Classifying problems with real-valued features.
method Empirical comparison of classifiers from three families: SVM, tree-based ensembles, and neural networks.
result Rotation forest is significantly more accurate than competing techniques on average.
A package for classification using conformal prediction methods.
problem Improving the reliability of machine learning predictions.
method Transductive and Inductive Conformal Prediction based on random forest.
result Computational efficiency and validity of predictions.
AMF is an online random forest algorithm for regression tasks.
problem Online learning for regression tasks with limited data.
method Mondrian Forests combined with Context Tree Weighting for efficient aggregation.
result AMF achieves competitive performance with optimal pruning, suitable for unknown function regularity.
GeFs use deep generative models to enhance prediction robustness and uncertainty.
problem Lack of principled methods to manipulate uncertainty in decision trees and random forests.
method Exploits Generative Forests (GeFs), a deep probabilistic model that extends Random Forests to represent full joint distributions.
result GeFs are uncertainty-aware classifiers capable of measuring robustness and detecting out-of-distribution samples.
ABRF uses attention weights to improve RF performance.
problem Improving the performance of random forest models.
method Attention mechanism applied to random forest with optimization and gradient-based methods.
result The proposed ABRF models outperform standard RF models on various datasets.
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…
Uncharted Forest visualizes data associations for classification and provenance studies.
problem Exploratory data analysis in high-dimensional datasets.
method Unsupervised tree ensemble (uncharted forest) for partitioning and visualizing data.
result Visualizes class associations, sample associations, and class heterogeneity.
SDF adapts Deep Forest for evolving data streams with active learning.
problem Adapting Deep Forest for evolving data streams.
method Streaming Deep Forest (SDF) with Augmented Variable Uncertainty (AVU) active learning.
result SDF with AVU outperforms other methods trained with all instances by 70% labeling budget.
Improved analysis of a random forest model reduces prediction error.
problem Improving prediction accuracy in random forest models.
method Revisited a historical random forest model, analyzing feature selection and splits.
result Mean-squared prediction error rate is improved to O((n(\log n)^{(S-1)/2})^{-\frac{1}{S\log2+1}}).