Paper proves stacking ensembling is effective and proposes a new family of stacked generalizations.
problem Lack of theoretical guarantees for stacking ensembling methods.
method Proves novel theoretical result and proposes a new family of stacked generalizations.
result Proves stacking ensembling is effective and proposes a new family of stacked generalizations.
MetaStackVis aids in choosing better metamodels for stacking ensembles.
problem Difficulty in selecting optimal metamodels for stacking ensembles.
method Interactive visualization tool to explore and compare different metamodels.
result Alternative metamodels significantly improve stacking ensemble performance.
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.
Paper combines multiple ETA models into a stacked ensemble for better ETA predictions.
problem Improving ETA predictions for taxi schedules and trips.
method Developed a two-level stacked ensemble model and applied XAI methods to explain it.
result The stacked ensemble model outperforms previous ETA approaches.
The motivation of this work is to improve the performance of standard stacking approaches or ensembles, which are composed of simple, heterogeneous base models, through the integration of the generation and selection stages for regression problems. We propose two extensions to the standard stacking approach. In the fir…
Active stacking improves heart rate estimation accuracy with minimal labeled data.
problem Inconsistent heart rate estimation across subjects due to signal quality and individual differences.
method Active learning and stacking ensemble regression to aggregate base estimators.
result Active stacking significantly outperforms other methods with minimal labeled data.
StackGenVis simplifies ensemble learning by visualizing model selection and performance.
problem Complexity in choosing and combining models for stacking ensemble learning.
method Visual analytics system that dynamically adapts performance metrics, manages data instances, selects algorithms, and measures predictive performance.
result Reduces complexity of stacking by removing overpromising and underperforming models.
Paper uses stacking with neural networks to predict cryptocurrency price direction.
problem Predicting the direction of cryptocurrency prices.
method Generative and discriminative classifiers stacked over a one-layer neural network, using technical indicators and sentiment analysis.
result Stacking method outperformed individual models in accuracy.
Two new ensemble methods improve CATE estimation across various scenarios.
problem Estimating CATE in clinical trials to understand treatment effects heterogeneity.
method Proposed two ensemble methods: Stacked X-Learner and Consensus Based Averaging (CBA).
result Ensemble methods achieve good performance across diverse scenarios.
A method to improve gradient boosting models using stacking.
problem Improving the performance of gradient boosting models.
method Proposes a stacking algorithm to learn a meta-model for ensembles of gradient boosting models.
result The proposed approach can be extended to differentiable combination models like neural networks.
RocketStack integrates predictions from multiple base learners using a recursive stacking architecture up to ten levels.
problem Feature redundancy, complexity, and computational burden in deep stacking.
method Level-aware recursive stacking with pruning and compression techniques.
result Increasing accuracy with depth and outperforming standalone ensembles at later levels.
Optimal ensemble construction improves prediction accuracy for multi-study tasks, especially in pandemic scenarios.
problem Poor out-of-study prediction performance due to heterogeneous datasets.
method Optimal ensemble construction using a two-stage stacking strategy that jointly estimates ensemble weights and study-specific model parameters.
result Our method outperforms multi-study stacking and other standard methods in predicting excess mortality during the pandemic.
Machine learning predicts Bitcoin price with high accuracy.
problem Uncertainty in Bitcoin price prediction for investors.
method Used machine learning techniques with technical indicators.
result Stacking ensemble model with random forest and GLM is optimal.
Stacked conformal prediction simplifies model validation.
problem Validating stacked predictive models efficiently.
method Meta-learner at the top of a stacked ensemble for approximate marginal validity.
result The method achieves approximate marginal validity without a separate calibration sample.
CDST improves ensemble prediction by adjusting model weights based on covariates.
problem Improving ensemble prediction accuracy in complex scenarios.
method Covariate-dependent stacking (CDST) with flexible model weights estimated via cross-validation.
result CDST consistently outperforms conventional model averaging methods in complex datasets.
AGM improves model accuracy through adaptive generation and feature augmentation.
problem Improving model accuracy in machine learning competitions.
method Adaptive Generation Model (AGM) using stacked ensemble learning with horizontal and vertical model expansion and feature augmentation.
result AGM outperforms previous models in 7 data sets.
The paper uses transfer stacking to improve tropical cyclone intensity prediction.
problem Challenging tropical cyclone intensity prediction due to climate changes.
method Transfer stacking and conventional neural networks for improving prediction performance.
result Transfer stacking enhances generalization in predicting tropical cyclone intensity.
Study develops ensemble machine learning framework for predicting groundwater heavy metal pollution.
problem Statistical complexity and spatial heterogeneity of heavy metal contamination in groundwater.
method Nested cross-validated ensemble machine learning with response transformations (raw, log, Gaussian copula).
result Copula-based models with DBSCAN clustering diagnostics provide the most reliable and interpretable assessments of groundwater contamination.
Gestalt combines two models to improve SQuAD2.0 performance.
problem Improving the accuracy of answering questions in context paragraphs.
method A stacking ensemble of ALBERT and RoBERTa models, combined with a CNN-based meta-model.
result Best ensemble achieved 87.117 EM and 90.306 F1 scores, improving baseline by 0.55% and 0.61% respectively.
Study predicts heart failure patient survival using stacked ensemble ML.
problem Predicting survival of heart failure patients.
method Collect and analyze patient data, apply SMOTE, use K-Means, Fuzzy C-Means clustering, Random Forest, XGBoost, Decision Tree, and propose a stacked ensemble model.
result Supervised ML algorithms outperform unsupervised models, achieving high accuracy and F1 score.
Proposes OBS, a method to adaptively combine Bayesian models online.
problem Learning optimal combinations of Bayesian models in online learning.
method Empirical Bayes lens, Online Bayesian Stacking (OBS).
result Establishes a novel connection between OBS and portfolio selection.
Stacked regressions improve predictive accuracy by combining estimators.
problem Improve predictive accuracy in regression models.
method Analogous to least-squares, learn combination weights by minimizing regularized empirical risk with nonnegativity constraint.
result The stacked estimator has strictly smaller population risk than the best single estimator, especially when signal-to-noise ratio is small.
We propose a novel stacked generalization (stacking) method as a dynamic ensemble technique using a pool of heterogeneous classifiers for node label classification on networks. The proposed method assigns component models a set of functional coefficients, which can vary smoothly with certain topological features of a n…
GOOWE-ML ensemble improves multi-label stream classification.
problem Online multi-label data stream classification.
method Novel online stacked ensemble with spatial weighting.
result GOOWE-ML ensembles outperform other models in predictive performance.
Study forecasts vegetable prices in Nepal using a novel index and ensemble model.
problem High volatility and cultural influences on agricultural commodity prices.
method Developed KVPI, created features, evaluated multiple models, introduced Momentum-Corrected Online Stacking Ensemble.
result Achieved RMSE of 1.771, MAPE of 0.68%, and R-squared of 0.845 at 90-day horizon.
Proposes a deep learning method to estimate virtual battery parameters from end-use measurements.
problem Estimating virtual battery parameters from limited load information.
method Transfer learning based stacked autoencoder for deep network framework.
result Effectively estimates virtual battery parameters for different load ensembles.
Paper proposes an ensemble model for writer-independent offline signature verification using deep learning.
problem Difficulty in distinguishing genuine signatures from skilled forgeries in writer-independent offline signature verification.
method Used an ensemble model with two CNNs for feature extraction, RGBT for classification, and stacking for final prediction.
result Achieved state-of-the-art performance on various datasets.
New method for disaggregate electricity demand forecasting at household level.
problem Challenges in forecasting electricity demand at individual household level.
method Additive stacking method for probabilistic disaggregate electricity demand forecasting.
result Improved accuracy in disaggregate electricity demand forecasting.
Stacking improves deep neural network training efficiency.
problem Improving the efficiency of training deep neural networks.
method Proposes stacking as a form of accelerated gradient descent.
result Proves stacking provides accelerated training for certain deep linear residual networks.
Amobee won 3rd and 1st place in SemEval 2018 sentiment classification tasks.
problem Sentiment classification in multiple languages.
method Training GRU-CNN model with word embeddings and stacking ensembles.
result 3rd and 1st place in valence ordinal classification sub-tasks in English and Spanish.
A new machine learning framework called machine collaboration improves prediction accuracy.
problem Improving prediction accuracy in machine learning.
method Machine Collaboration (MaC) framework, which uses a circular and interactive learning approach.
result Machine Collaboration framework significantly outperforms other state-of-the-art methods in most cases.
Deep learning ensemble improves Alzheimer's disease classification accuracy.
problem Improving diagnostic accuracy for Alzheimer's disease.
method Proposes a deep ensemble learning framework integrating multisource data and expert wisdom.
result 4% improvement in classification accuracy compared to existing methods.
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.
Super learner uses diverse screeners to improve prediction performance.
problem Performance issues with lasso screening in super learner.
method Used a diverse set of candidate screeners within the super learner ensemble.
result Diverse screeners protect against poor performance of any one screener.
Classification outperforms regression in portfolio construction, yielding higher Sharpe ratios.
problem Determining which machine learning approach (classification vs. regression) is more effective for portfolio construction.
method Used stacking ensemble of gradient boosted tree, random forest, and neural network models.
result Classification yields higher Sharpe ratios and economically significant alphas compared to regression.
FASE-AL uses active learning to reduce labeling costs for data streams.
problem Reduction of labeling costs for data stream classification.
method Combines Fast Adaptive Stacking of Ensembles (FASE) with active learning.
result Achieves high accuracy with minimal labeled data.
LESS combines local predictors for subsets to learn from heterogeneous input-output pairs.
problem Learning from heterogeneous input-output pairs in populations with varied behavior.
method LESS algorithm: generates subsets, trains local predictors, combines them.
result LESS is highly competitive compared to state-of-the-art methods.
Study examines unsupervised and graph-based methods for anomaly detection in IoBT, outperformed by supervised stacking ensemble.
problem Anomaly detection in adversarial environments of IoBT.
method Unsupervised learning, graph-based methods, ensemble supervised learning, adversarial training.
result Supervised stacking ensemble method outperforms unsupervised and graph-based methods in detecting anomalies.
New ensemble models classify mouse movement trajectories to assess survey question difficulty.
problem Assessing survey question difficulty based on respondents' interaction data.
method Ensemble models combining semi-metric-based weak learners to classify multivariate functional data.
result Improved survey data quality through better identification of respondent difficulty.
Paper tackles multi-source domain adaptation for regression.
problem Predicting HDL cholesterol levels using gut microbiome data.
method Two-step procedure: 1) Extend a flexible single-source DA algorithm for classification to regression. 2) Augment with ensemble learning for multi-source DA.
result Consistent improvement in HDL cholesterol level prediction performance over existing methods.
MAC combines models without locking them, improving ensemble performance.
problem Improving ensemble learning performance with flexibility.
method Model agnostic combination technique that dynamically combines models.
result MAC outperforms classical methods and competitive to boosting.
A new framework explains mixed models by propagating Shapley values.
problem Making complex models like neural networks and stacked models explainable for healthcare applications.
method DeepSHAP framework for layer-wise propagation of Shapley values.
result DeepSHAP enables attributions for mixed models and theoretically justifies attributions with respect to a background distribution.
Combination of distributional regression algorithms improves uncertainty estimation of satellite precipitation products.
problem Uncertainty estimation in satellite precipitation products.
method Ensemble learning methods combining conditional zero-adjusted probability distributions estimated with GAMLSS, spline-based GAMLSS, and distributional regression forests.
result Stacking of methods outperformed individual methods in most quantile levels using the quantile loss function.
The paper proposes a demand prediction model for e-commerce sites using machine learning and stacking.
problem Accurately predicting demand for products sold by multiple sellers at different prices.
method Applied different regression algorithms and stacked generalization for demand prediction.
result Stacked generalization produced almost as good results as individual machine learning methods.
Machine learning improves CHD screening accuracy from 70% to 87.7%.
problem Predicting coronary heart disease using echocardiography and clinical features.
method Ensemble machine learning approach with model stacking and two-step stacking.
result Improved CHD classification accuracy from 70% to 87.7%.
FAST-DAD distills complex ensemble models into faster, more accurate individual models.
problem Deploying complex AutoML ensemble predictors on tabular data is slow, large, and opaque.
method Data augmentation strategy based on Gibbs sampling from a self-attention pseudolikelihood estimator.
result FAST-DAD distillation produces significantly better individual models than standard training.
iPrescribe offers fast online offer recommendations using deep learning.
problem Online offer recommendation in real-time.
method Ensemble of deep learning and machine learning algorithms, optimized streaming technology stack, and efficient LSTM deployment.
result 90th percentile recommendation latency of 38 milliseconds.
This work improves SSL by leveraging disentangled latent space for better self-ensembling.
problem Improving semi-supervised learning performance with limited labeled data.
method Stacked SSL model using unsupervised disentangled representation learning for stochastic embedding.
result Improved performance and interpretability of disentangled representations over related SSL models.