Enhances apparel attribute recognition with a two-layer ensemble method.
problem Improving accuracy in apparel attributes classification using deep neural networks.
method Proposes a two-layer mixture framework combining bagging and boosting for ensemble learning.
result The proposed method outperforms individual models and ensemble methods.
KNN ensembles improve Tweedie regression performance.
problem Improving regression models for Tweedie distribution.
method Combining bagged features, bagged observations, and varied k in KNN ensembles.
result Varying k provides significant improvements in model fit and robustness.
Bagging stabilizes linear interpolators, improving their generalization performance.
problem Unstable linear interpolators fail on noisy data.
method Introduced multiplier-bootstrap-based bagged least square estimator.
result Bagging effectively mitigates variance, leading to bounded prediction risk.
In multiple instance learning, objects are sets (bags) of feature vectors (instances) rather than individual feature vectors. In this paper we address the problem of how these bags can best be represented. Two standard approaches are to use (dis)similarities between bags and prototype bags, or between bags and prototyp…
WildWood improves Random Forest predictions using bootstrap out-of-bag samples.
problem Improving Random Forest predictions for supervised learning.
method Uses bootstrap out-of-bag samples to compute improved predictions by aggregating all possible subtrees with exponential weights.
result WildWood produces faster and more competitive predictions compared to other ensemble methods.
Feature bagging improves stability through random feature subsampling.
problem Improving the stability of ensemble learning methods.
method Introducing feature instability (FI) and analyzing feature bagging in parametric and model-free settings.
result Feature bagging provides stronger stability than non-bagged methods, especially with aggressive subsampling.
Estimates algorithmic variance for bagging and random forests using bootstrap.
problem Deciding when an ensemble is large enough for accurate predictions.
method Bootstrap method to estimate algorithmic variance under a first-order model.
result Consistent approximation of the centered law of prediction error as ensemble size increases.
A new method combines bagging and threshold-moving for imbalanced data.
problem Class imbalance in data mining hinders model performance.
method Probability thresholding bagging (PT-bagging) that preserves natural class distribution.
result PT-bagging improves model performance on imbalanced data.
Theory and method for reducing prediction variance in noisy feature-subsampled ridge ensembles.
problem Reduction of prediction variance in noisy data with feature bagging.
method Developed analytical learning curves for noisy ridge ensembles, introduced heterogeneous feature ensembling.
result Subsampling shifts the double-descent peak, leading to improved performance over a single linear predictor.
Under-bagging k-NN improves performance on imbalanced classification.
problem Imbalanced classification problems where one class is significantly underrepresented.
method Proposes an under-bagging k-NN ensemble learning algorithm, analyzing convergence rates and efficiency. result Achieves optimal convergence rates under mild assumptions and reduces sub-sample size and k for highly imbalanced data. A new ensemble method reduces noise by prioritizing hard instances.
problem Noise and outliers in training data affect ensemble methods' performance.
method Probability of instance selection inversely proportional to its hardness.
result Accuracy significantly better in high noise scenarios.
Paper uses ensemble learning for more accurate power flow modeling.
problem Improving accuracy and efficiency of power flow modeling.
method Applies polynomial regression and ensemble learning (GB, bagging) to create a more accurate linear power flow model.
result Data-driven model outperforms traditional methods in accuracy and speed.
Random forests reduce bias and variance, especially in low SNR settings.
problem Reducing bias and variance in machine learning models, particularly in low SNR scenarios.
method Empirical study of random forests and bagging ensembles, focusing on the importance of mtry tuning. result Random forests reduce both bias and variance, outperforming bagging ensembles in high SNR settings.
Unified framework for cost-sensitive ensemble learning.
problem Different misclassification costs in data.
method A unifying framework for cost-sensitive ensemble methods.
result Unified understanding and natural extensions of existing methods.
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.
A new method combines quantile regression, cross-conformalization, and out-of-bag predictions for valid prediction sets.
problem Valid prediction sets for classification and regression without distributional assumptions.
method Nested conformal prediction framework, combining quantile regression, cross-conformalization, and out-of-bag predictions.
result QOOB algorithm performs best or close to best on all simulated and real datasets.
Internal node bagging uses dropout-like training to improve model fitting with fewer parameters.
problem Improving model fitting with fewer parameters.
method Explicitly forces a group of nodes to learn a certain feature, combining them in inference time.
result Internal node bagging performs significantly better than dropout on small models.
Proposes a novel Out-of-Bag anomaly detection method for ML systems.
problem Challenges of detecting data anomalies in real-world datasets.
method Model-based approach decomposing unsupervised problem into ensemble models using Out-of-Bag estimates.
result Demonstrates state-of-the-art performance and improved accuracy in ML systems.
ML-KFHE improves multi-label classification performance using Kalman filter fusion.
problem Lack of effective multi-label ensemble classification methods.
method Exploits Kalman filter sensor fusion properties for multi-label classification.
result Significantly improved predictive performance on multi-label datasets.
Improved tree selection methods enhance OTE's performance.
problem Optimal trees ensemble (OTE) underperforms with larger training data.
method Two modified methods: OOB and sub-bagging.
result Improved predictive accuracy compared to OTE and other methods.
Optimizes ensemble weights and hyperparameters for better machine learning model predictions.
problem Improving ensemble model performance through optimal weights and hyperparameters tuning.
method Designing a nested optimization algorithm that tunes hyperparameters and finds optimal ensemble weights, using Bayesian search and a heuristic for diverse base learners.
result The algorithm (GEM-ITH) produces better ensemble model performance across various datasets.
VisRuler simplifies decision extraction from bagged and boosted trees.
problem Complexity and lack of interpretability in ensemble models.
method Visual analytics tool for selecting robust models, important features, and essential decisions.
result Users successfully extracted and explained decisions from ensemble models.
The paper analyzes the risk of bagging regularized M-estimators under proportional asymptotics.
problem Characterizing the risk of ensemble estimators trained with subsamples and regularizers.
method Developed a consistent estimator for the risk of ensemble estimators under proportional asymptotics.
result Optimal subsample size k⋆ tends to be in the overparameterized regime for the full-ensemble estimator. Paper proposes an online transfer learning framework using online bagging.
problem Difficulty in obtaining sufficient labeled data in the target domain.
method Ensemble approach with online bagging for anytime transfer learning.
result Effectiveness of the proposed algorithms demonstrated on real data sets.
The paper analyzes bagging in overparameterized learning, deriving risk properties and optimal subsample sizes.
problem Characterizing the risk of bagged predictors in overparameterized settings.
method General strategy using classical results on simple random sampling, specialized for ridge and ridgeless predictors.
result Derives exact asymptotic risk of bagged ridge and ridgeless predictors under various conditions.
ECV method optimizes ensemble parameters for randomized ensembles.
problem Efficient tuning of ensemble parameters in randomized ensembles.
method ECV (Extrapolated Cross-Validation) method for tuning ensemble and subsample sizes.
result ECV yields δ-optimal ensembles for squared prediction risk.
Random forests improve by using out-of-bag samples for post-pruning.
problem Improving the generalization error of random forests.
method Post-pruning using out-of-bag samples to improve decision trees and random forest size.
result Consistent decrease in forest size with no significant loss in accuracy.
Bagging improves sparse regression performance, especially with reduced sampling ratios.
problem Improving sparse regression performance in low measurement scenarios.
method Generalized Bagging with various bootstrap sampling ratios.
result Bagging outperforms L1 minimization and Bolasso in challenging sparse regression cases.
Study compares under-bagging with other methods for imbalanced data.
problem Comparing under-bagging with other methods for imbalanced data.
method Replica analysis of under-bagging, comparing with under-sampling and simple weighting.
result Under-bagging improves performance by increasing the majority class size.
BDMBC clusters data with varying densities using a new PLLS measure.
problem Finding clusters with varying densities in data.
method Bagged k-distance with PLLS for mode estimation. result BDMBC achieves optimal convergence rates for mode and level set estimation.
Optimal survival trees ensemble reduces tree count and improves predictive performance.
problem Improving predictive performance in survival analysis.
method Grows a forest of optimal survival trees by ranking and selecting the best trees based on out-of-bag error.
result Reduces the number of trees in the ensemble while improving predictive performance.
A new ensemble method improves kNN performance by extending the neighborhood rule.
problem Traditional kNN's limitations when test points are outside the spherical region and ensemble's high errors.
method Determines neighbors in k steps, using bootstrap samples and optimal models selection.
result The proposed ensemble method outperforms state-of-the-art methods on 17 benchmark datasets.
Paper introduces r-DEP classifier for binary classification tasks.
problem No natural ordering for feature patterns in practical situations.
method Introduces reduced dilation-erosion (r-DEP) classifier using multi-valued mathematical morphology.
result r-DEP classifiers outperform traditional SVCs in balanced accuracy.
MetaBags improves regression ensemble performance by selecting diverse base models.
problem Challenges in learning heterogeneous regression ensembles.
method MetaBags is a novel stacking framework that learns a set of meta-decision trees to select base models for each query.
result MetaBags significantly outperforms existing state-of-the-art approaches in regression tasks.
Bagging reduces variance in LID estimation by preserving local distribution of NN distances.
problem High estimation variance from limited data in small neighborhoods.
method Subbagging to preserve local distribution of NN distances, combined with ensemble size.
result Bagging significantly reduces variance and MSE in LID estimation.
While both cost-sensitive learning and online learning have been studied extensively, the effort in simultaneously dealing with these two issues is limited. Aiming at this challenge task, a novel learning framework is proposed in this paper. The key idea is based on the fusion of online ensemble algorithms and the stat…
A new adaptive kNN classifier outperforms Random Forests.
problem Improving classification accuracy using nearest neighbors.
method Finding discriminant subspaces for efficient nearest neighbor classification, leveraging bagging for diversity.
result The proposed method outperforms Random Forests and other nearest neighbors ensembles.
Improved TSC with BOSS and SP techniques.
problem Comparing BOP and BOSS for time series classification.
method Deconstructed and measured components of BOP and BOSS, adapted CV techniques.
result SP with BOSS significantly more accurate than benchmarks.
autoBagging learns to rank bagging workflows for better ML performance.
problem Finding the best bagging workflow for each dataset.
method Metalearning and learning to rank to automatically rank bagging workflows.
result autoBagging outperforms Average Rank and achieves results not statistically different from an ideal model.
Enhanced TSFMs improve time series forecasting accuracy and reliability.
problem Variance, bias, and uncertainty in TSFMs' predictions on real data.
method Statistical and ensemble techniques including bagging, stacking, residual modeling, and prediction intervals.
result Hybrid models consistently outperform standalone TSFMs across multiple horizons.
A new clustering method using Bayesian techniques improves robustness and interpretability.
problem Improving clustering techniques for better robustness and interpretability.
method The paper proposes a novel Bayesian clustering method using the proper Bayesian bootstrap, which combines k-means clustering and ensemble clustering.
result The method provides clear indication on the optimal number of clusters and a better representation of the clustered data.
Convex regression is a promising area for bridging statistical estimation and deterministic convex optimization. New piecewise linear convex regression methods are fast and scalable, but can have instability when used to approximate constraints or objective functions for optimization. Ensemble methods, like bagging, sm…
A new tree-based model improves uncertainty estimation in sequential optimization.
problem Improving uncertainty estimation in sequential model-based optimization.
method Proposed a new ensemble of randomized trees (BwO forest) with bagging and oversampling.
result BwO forest outperforms existing tree-based models in various optimization scenarios.
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.
Overview of SML techniques with banking applications.
problem Credit risk modeling in banking.
method Tree-based ensemble algorithms, Feedforward NNs, hyper-parameter optimization, machine learning interpretability.
result Comparison of ML algorithm features and their application in banking.
Bootstrap aggregation, known as bagging, is one of the most popular ensemble methods used in machine learning (ML). An ensemble method is a ML method that combines multiple hypotheses to form a single hypothesis used for prediction. A bagging algorithm combines multiple classifiers modeled on different sub-samples of t…
Enhances multi-class classification using neural networks and decision trees.
problem Improving multi-class classification accuracy.
method Combines neural networks, decision trees, and random vector functional link networks with oblique decision surfaces.
result Superior performance on multi-class datasets compared to state-of-the-art classifiers.
Paper improves DNS typo-squatting detection with ensemble model.
problem Detecting and preventing DNS typo-squatting attacks.
method Ensemble-based feature selection and bagging classification model.
result The proposed framework achieves high accuracy and precision in identifying typo-squatting domains.