Efficient method for resampling problems using vector approximate message passing.
problem Computational demand in resampling techniques for statistical inference and ensemble learning.
method Combination of replica method from statistical physics and vector approximate message passing from information theory.
result Fast convergence and high approximation accuracy for variable selection problems.
Optimal model improves AUC, recall, and F1 score for class-imbalanced business risk.
problem Improving prediction of class-imbalanced business risk.
method Resampling, regularization, and model ensembling techniques.
result Boosting on DT with SMOTE oversampling achieves AUC, recall, and F1 score of 0.8633, 0.9260, and 0.8907, respectively.
Framework for renewable energy forecasting and feature engineering.
problem Forecasting and feature extraction for multivariate processes in renewable energy.
method Derivative-free optimization, ensemble of sequence-to-sequence networks, additive resampling, Bootstrap aggregating.
result The proposed method outperforms other machine learning techniques in long-term forecasts and feature selection.
We propose and evaluate alternative ensemble schemes for a new instance based learning classifier, the Randomised Sphere Cover (RSC) classifier. RSC fuses instances into spheres, then bases classification on distance to spheres rather than distance to instances. The randomised nature of RSC makes it ideal for use in en…
MULTIFIT tests independence between two random vectors using multiscale Fisher's test.
problem Detecting local dependence between two random vectors.
method MULTIFIT uses a resampling-free approach to test independence.
result MULTIFIT can easily handle large sample sizes and interpret dependency nature.
The combination of multiple classifiers using ensemble methods is increasingly important for making progress in a variety of difficult prediction problems. We present a comparative analysis of several ensemble methods through two case studies in genomics, namely the prediction of genetic interactions and protein functi…
Simulation studies show resampling methods can be reliable for causal graph confidence.
problem Determining when causal discovery results can be trusted in real-world settings.
method Evaluation of subsampling and sampling with replacement methods.
result Subsampling and sampling with replacement performed well in indicating graph feature confidence.
Study tackles workplace injury prediction and prevention.
problem Rare and imbalanced data makes injury risk prediction challenging.
method Ensemble resampling, transfer learning, and variable analysis.
result Improved injury risk prediction and prevention techniques.
Class imbalance problems manifest in domains such as financial fraud detection or network intrusion analysis, where the prevalence of one class is much higher than another. Typically, practitioners are more interested in predicting the minority class than the majority class as the minority class may carry a higher misc…
Proposes a method to reduce ensemble size while maintaining accuracy.
problem Complexity and computational burden of ensemble models in large-scale data.
method Optimizes margin distribution to reduce ensemble size while increasing diversity.
result Pruned ensemble uses only a fraction of original classifiers with improved or similar generalization performance.
Class imbalance presents a major hurdle in the application of data mining methods. A common practice to deal with it is to create ensembles of classifiers that learn from resampled balanced data. For example, bagged decision trees combined with random undersampling (RUS) or the synthetic minority oversampling technique…
In this paper we present a technique for using the bootstrap to estimate the operating characteristics and their variability for certain types of ensemble methods. Bootstrapping a model can require a huge amount of work if the training data set is large. Fortunately in many cases the technique lets us determine the eff…
Statistical analysis reveals pitfalls in climate network construction.
problem Uncertainty in correlation estimates affects network characteristics.
method Simulated isotropic random fields and resampling procedures.
result Spurious link bundles and high-degree clusters are common.
Deep Neural Networks (DNNs) have become increasingly popular in computer vision, natural language processing, and other areas. However, training and fine-tuning a deep learning model is computationally intensive and time-consuming. We propose a new method to improve the performance of nearly every model including pre-t…
Boosting Nyström improves accuracy of matrix approximations.
problem Generating low-rank approximations of large matrices efficiently.
method Iteratively generate multiple weak Nyström approximations, combine them to form a strong approximation.
result Boosting Nyström yields more efficient and accurate low-rank approximations.
A new method estimates time-varying parameters in earth system models using offline and online data assimilation.
problem Estimating time-varying parameters in complex earth system models.
method Hybrid Offline Online Parameter Estimation with Particle Filtering (HOOPE-PF)
result HOOPE-PF outperforms existing methods, especially with small ensemble sizes.
This paper surveys various data balancing methods for imbalanced datasets.
problem Imbalanced datasets bias predictions and degrade classifier performance.
method Extensive review of oversampling, undersampling, adaptive, generative, combination, and ensemble methods.
result No single method universally outperforms others; selection depends on dataset characteristics.
A framework infers feature importance with uncertainties for high-dimensional data.
problem Estimating feature importance in high-dimensional data with uncertainty.
method Shapley value based framework, sub-SAGE, bootstrapping.
result Uncertainties in feature importance can be estimated from bootstrapping.
This study proposes a method to predict ICU infections from imbalanced data using clustering-based undersampling and ensemble classifiers.
problem Predicting healthcare-associated infections in ICU patients from imbalanced data.
method Clustering-based undersampling strategy combined with ensemble classifiers.
result The proposed method outperforms other resampling techniques in predicting ICU infections.
Differentiable resampling improves particle filter performance.
problem Non-differentiability of traditional resampling in particle filters.
method Introduced a neural network resampler (particle transformer) trained with a likelihood-based loss function.
result Learned resampling outperforms traditional methods on synthetic and real-world tasks.
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.
A new differentiable resampling method for Monte Carlo simulations.
problem Improving the efficiency and differentiability of resampling in Monte Carlo simulations.
method Proposes a diffusion model surrogate for resampling, proving consistency and outperforming existing methods.
result The proposed method outperforms state-of-the-art differentiable resampling methods on various benchmarks.
We present a novel approach to learn binary classifiers when only positive and unlabeled instances are available (PU learning). This problem is routinely cast as a supervised task with label noise in the negative set. We use an ensemble of SVM models trained on bootstrap resamples of the training data for increased rob…
Study improves bankruptcy prediction models for imbalanced data and economic stages.
problem Class imbalance and economic fluctuations in credit risk analytics.
method Applied 8 BPMs on a new public dataset with resampling and AI techniques.
result Models perform differently during economic changes.
New method reduces uncertainty in deep neural networks with minimal computation.
problem Uncertainty in over-parameterized neural networks hinders reliability and statistical guarantees.
method Procedural-noise-correcting (PNC) predictor and resampling methods.
result Asymptotically exact-coverage confidence intervals constructed with minimal computation.
Persistent sampling improves SMC efficiency by retaining and reusing particles.
problem High computational costs and particle impoverishment in SMC.
method Persistent sampling (PS) retains and reuses particles from all prior iterations, using multiple importance sampling and resampling from a mixture of historical distributions.
result PS achieves more accurate posterior approximations and lower variance in marginal likelihood estimates without additional likelihood evaluations.
In many real-world binary classification tasks (e.g. detection of certain objects from images), an available dataset is imbalanced, i.e., it has much less representatives of a one class (a minor class), than of another. Generally, accurate prediction of the minor class is crucial but it's hard to achieve since there is…
In Machine Learning, ensemble methods have been receiving a great deal of attention. Techniques such as Bagging and Boosting have been successfully applied to a variety of problems. Nevertheless, such techniques are still susceptible to the effects of noise and outliers in the training data. We propose a new method for…
fastml guards against data leakage in automated machine learning.
problem Data leakage during preprocessing before resampling inflates apparent performance.
method fastml uses guarded resampling to re-estimate preprocessing inside each resample.
result Guarded resampling reduces apparent performance compared to global preprocessing.
To use machine learning in high stakes applications (e.g. medicine), we need tools for building confidence in the system and evaluating whether it is reliable. Methods to improve model reliability often require new learning algorithms (e.g. using Bayesian inference to obtain uncertainty estimates). An alternative is to…
A new resampling strategy, Importance Resampling, improves sample efficiency and reduces variance in off-policy prediction.
problem High variance updates in importance sampling for off-policy prediction.
method Importance Resampling (IR) resamples experience from a replay buffer and applies standard on-policy updates, avoiding importance sampling ratios.
result Importance Resampling (IR) shows improved sample efficiency and lower variance updates compared to other methods.
This paper proposes neural network-based undersampling techniques to improve model performance on class-imbalanced datasets.
problem Class imbalance problem in machine learning models leads to biased predictions and lower performance metrics.
method Neural network-based undersampling techniques applied to class-imbalanced datasets.
result Neural network-based undersampling outperforms other resampling techniques in terms of AUC, F1, and G-mean scores.
New law predicts first extinction in resampling processes.
problem Intractable extinction times in resampling processes.
method Modeling multinomial updates as independent square-root diffusions.
result Closed-form law for first-extinction time with linear cost.
A new method for synthetic oversampling of multi-label data focusing on local label distribution.
problem Class imbalance in multi-label datasets affects prediction accuracy.
method Proposes a new method for synthetic oversampling of multi-label data focusing on local label distribution.
result Demonstrates effectiveness in generating more diverse and better labeled instances.
This paper tackles noisy multi-objective optimization with adaptive resampling using bootstrapping.
problem Challenges in optimizing noisy multi-objective problems, especially trade-offs between exploration and exploitation.
method Adaptive resampling with bootstrapping to estimate probability of dominance and improve precision.
result Demonstrates the efficiency of the resampling approach in NSGA-II algorithm under multiple noise variations.
Probabilistic graphical models are graphical representations of probability distributions. Graphical models have applications in many fields including biology, social sciences, linguistic, neuroscience. In this paper, we propose directed acyclic graphs (DAGs) learning via bootstrap aggregating. The proposed procedure i…
This work proves convergence of adaptive resampling for random Fourier features.
problem Sampling Fourier frequencies well for high-dimensional data.
method Data adaptive resampling of Fourier frequencies, asymptotically optimal.
result Proves convergence of adaptive resampling method for regression and classification problems.
Generates diverse images by resampling specific parts while maintaining global consistency.
problem Creating diverse images while maintaining global consistency in certain parts.
method Developed a new network architecture, training procedure, and resampling algorithm.
result Achieved low distortion block-resampling with spatially stochastic networks.
This review explores resampling techniques for imbalanced binary classification.
problem Imbalanced classes lead to poor prediction results in classification.
method Classical, cost-sensitive, and Neyman-Pearson paradigms with resampling techniques and classification methods.
result Complex dynamics among resampling techniques, base methods, metrics, and imbalance ratios.
DAIS improves AIS by resampling, avoiding gradient issues.
problem Low effective sample size in DAIS.
method DAIS with resampling step to improve efficiency.
result Resampling step avoids gradient variance issues.
Study shows resampling can drastically alter PCA results.
problem Stability and sensitivity of PCA under data resampling.
method Analyzed resampling sensitivity of high-dimensional PCA.
result PCA's principal components become asymptotically orthogonal when resampling is significant.
Study compares resampling methods for rare event prediction in longitudinal studies.
problem Predicting rare events in longitudinal follow-up studies.
method Comparison of resampling methods to improve standard regression models.
result Effect of sampling rate on model predictive performance.
Toy model study shows resampling/reweighting can improve feature learning in imbalanced classification.
problem Improving feature learning in imbalanced classification problems.
method High-dimensional toy model with replica method, class-wise resampling/reweighting, and simplified model.
result No resampling/reweighting can sometimes give best feature learning performance.
A new algorithm resamples Bernoulli race particle filters using true weights.
problem Handling intractable weights in particle filters.
method Proposes a novel resampling method using true weights with an unbiased estimator.
result Demonstrates lower variance in filtering estimates compared to standard methods.
This paper investigates bias in resampled backtests for financial portfolios, finding it often negligible.
problem Bias in resampled backtests for financial portfolio evaluation.
method Investigation of bias in rolling-window mean-variance portfolios using resampling techniques.
result The bias in Sharpe Ratio estimates from IID resampling is often a fraction of estimation noise, making it tolerable.
Resampling outperforms reweighting for correcting biased data in machine learning models.
problem Correcting sampling bias in machine learning models trained on biased data sets.
method Compared resampling and reweighting techniques, focusing on their performance with stochastic gradient algorithms.
result Resampling outperforms reweighting when combined with stochastic gradient algorithms.
Improved particle filters for estimating model parameters using differentiable resampling.
problem Inability to differentiate sampling and resampling steps in particle filters.
method Extended reparameterisation trick to include stochastic input, enabling differentiation. Used p-MCMC and NUTS for parameter estimation.
result NUTS improves mixing of Markov chain and produces more accurate results in less time.
This chapter reviews ML resampling methods for cybersecurity.
problem Estimating ML performance in cybersecurity.
method Resampling techniques for error rate and AUC estimation.
result Established a theoretical framework for ML resampling methods.