KFHE uses Kalman filters to improve ensemble classification accuracy.
problem Improving multi-class ensemble classification accuracy.
method KFHE treats ensemble training as a state estimation problem using Kalman filters.
result KFHE outperforms state-of-the-art algorithms in noisy and clean datasets.
Unsupervised ensemble classification for dependent data.
problem Classifying data with dependencies using multiple classifiers.
method Developed algorithms for sequential and networked data dependencies, using moment matching and Expectation Maximization.
result Improved classification performance on synthetic and real datasets.
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.
Efficient neural network ensembles improve image classification reliability and uncertainty quantification.
problem Uncertainty in neural network predictions for industrial image classification.
method Investigated efficient neural network ensembles (snapshot, batch, multi-input multi-output) for image classification reliability and uncertainty quantification.
result Batch ensemble is a cost-effective and competitive alternative to deep ensembles, offering savings in training and test time.
Research reveals how diversity impacts ensemble generalization in classification tasks.
problem Understanding the relationship between diversity and generalization in classification ensembles.
method Investigated diversity measurement, its relationship with generalization error, and pruning methods.
result Generalization error is reduced effectively only when diversity is increased in specific ranges, not in others.
RCAM-based ensemble combines binary classifiers using similarity and vote scheme.
problem Improving binary classification accuracy through ensemble methods.
method RCAM-based ensemble combining classifiers using similarity and recurrent consult-vote scheme.
result RCAM-based ensemble outperforms individual classifiers and majority voting.
Deep neural networks outperform traditional ensemble methods in time series classification.
problem Deep learning models struggle to match traditional ensemble methods in time series classification.
method Developed an ensemble of 60 deep learning models to improve time series classification performance.
result The proposed Neural Network Ensemble (NNE) outperforms current state-of-the-art methods.
Self-paced ensemble learning improves audio classification models.
problem Improving performance of individual models in speech and audio classification.
method A self-paced ensemble learning scheme where models learn from each other over several iterations.
result SPEL significantly outperforms baseline ensemble models.
Tree ensembles like RF and GBT can be seen as kernels, improving regression and classification performance.
problem Improving kernel methods for tree ensemble based models.
method Investigation of RF and GBT kernels in simulation and real data.
result RF and GBT kernels are competitive to their respective ensembles in higher dimensions, particularly with noisy features.
A new method speeds up uncertainty estimation in image classification.
problem Fast and accurate uncertainty estimation for robust robotics applications.
method Deep sub-ensembles, where only layers close to the output are ensembled.
result Significant speedup in uncertainty estimation with minimal error and NLL increase.
This paper explores methods for combining predictions in multilabel classification.
problem Lack of formal framework for aggregation in multilabel ensembles.
method Introduces two approaches: 'predict then combine' (PTC) and 'combine then predict' (CTP).
result Standard voting techniques are outperformed by tailored instantiations of CTP and PTC.
Study pitfalls of deep learning ensembles in uncertainty estimation.
problem Pitfalls in in-domain uncertainty estimation and ensembling in deep learning.
method Exploration of standards for uncertainty quantification and broad study of ensembling techniques.
result Many sophisticated ensembling techniques are equivalent to a simple ensemble of few networks.
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.
A new ensemble method using random projections for kNN classification.
problem Improving kNN classification accuracy through ensemble methods.
method Random projection of bootstrap samples into lower dimensions, using extended neighbourhood rule for base learners.
result Enhanced classification accuracy compared to traditional kNN and other ensembles.
Ensembles improve classifier performance by reducing bias, not variance.
problem Improving classifier performance through ensemble methods.
method Extended bias-variance decomposition for classification tasks, introducing dual reparameterization.
result Ensembling reduces bias in classifiers, contrary to the traditional view.
The study compares shallow and deep learning methods for text classification.
problem High-dimensional, sparse data from customer calls.
method Comparative evaluation of shallow, deep, and ensemble methods.
result Ensemble methods improve classification accuracy.
Proposes using Wasserstein barycenters for model ensembling in multiclass/multilabel learning.
problem Finding consensus between models in multiclass/multilabel learning settings.
method Uses Wasserstein (W.) barycenters to find consensus between models, incorporating semantic side information.
result Wasserstein ensembling balances confidence and semantics in model agreement.
In recent years, multi-label classification problem has become a controversial issue. In this kind of classification, each sample is associated with a set of class labels. Ensemble approaches are supervised learning algorithms in which an operator takes a number of learning algorithms, namely base-level algorithms and …
JUCAL jointly calibrates aleatoric and epistemic uncertainties in classifier ensembles.
problem Misrepresentation of predictive uncertainty due to unbalanced aleatoric and epistemic uncertainties.
method Joint Uncertainty Calibration (JUCAL) that jointly calibrates two constants to weight and scale uncertainties.
result Significantly outperforms state-of-the-art calibration methods across various text classification tasks.
Bayesian inference using stochastic neural networks ensembles.
problem Approximating Bayesian posterior distributions.
method Formulate stochastic ensembles of neural networks, train with variational inference, and evaluate using Monte Carlo dropout.
result Stochastic ensembles provide more accurate posterior estimates than other methods.
EnD2 distills ensemble diversity into a single model.
problem Loss of ensemble diversity information in distillation.
method Prior Networks for distilling ensemble distribution into a single model.
result EnD2 retains both ensemble performance and diversity information. 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.
Unified framework for supervised classification with diverse training data.
problem Handling different types of training data for supervised classification.
method Generalized robust risk minimization (GRRM) with probabilistic transformations.
result GRRM can handle various training data types and new supervision schemes.
XEM improves multivariate time series classification with explainable models.
problem Multivariate time series classification challenges.
method Hybrid ensemble method combining explicit boosting-bagging and implicit divide-and-conquer.
result XEM outperforms state-of-the-art MTS classifiers on public datasets.
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.
Ensemble techniques are powerful approaches that combine several weak learners to build a stronger one. As a meta learning framework, ensemble techniques can easily be applied to many machine learning techniques. In this paper we propose a neural network extended with an ensemble loss function for text classification. …
Ensemble GP improves genetic programming by achieving better results with smaller models.
problem Improving genetic programming for binary classification problems.
method Ensemble GP uses an evolved population structure, fitness evaluation, and genetic operators inspired by ensemble learning methods.
result Ensemble GP outperformed standard GP on eight binary classification problems, achieving better results with smaller models.
Specialists outperform generalists in ensemble classification.
problem Determining the accuracy of an ensemble of classifiers when individual classifier accuracies are known.
method Proved upper and lower bounds on ensemble accuracy, constructed specialist and generalist classifiers.
result Upper and lower bounds on ensemble accuracy, practical implications for classifier construction.
Paper proposes ECOC for deep neural network ensembles to improve performance.
problem Designing an ensemble of deep networks is time-consuming and often not beneficial.
method ECOC framework applied to deep networks, with design strategies to balance accuracy and complexity.
result Proposed combinatory technique achieves highest classification performance.
A novel method for classification with rejection using ensemble of cost-sensitive classifiers.
problem Avoid risky misclassification in error-critical applications.
method Learning an ensemble of cost-sensitive classifiers.
result Improved classification accuracy and flexibility in loss selection.
Paper presents a Bayesian-decision-theory framework for long-tailed classification.
problem Heavy imbalance and asymmetric misprediction costs in long-tailed datasets.
method Bayesian-decision-theory perspective, unifying re-balancing and ensemble methods.
result Improves accuracy for all classes, especially tails, with provably optimal decisions.
Enhances robustness for time series classification using self-ensemble method.
problem Limited adversarial robustness in time series classification.
method Proposes a self-ensemble method to improve Randomized Smoothing's robustness certification.
result Demonstrates superior robustness compared to baseline approaches.
This paper evaluates methods to improve classification on imbalanced datasets.
problem Class imbalance in classification problems.
method Combination of data augmentation and ensemble learning methods.
result Combinations of data augmentation methods with ensemble learning can significantly improve classification performance.
Ensembles of classifier models typically deliver superior performance and can outperform single classifier models given a dataset and classification task at hand. However, the gain in performance comes together with the lack in comprehensibility, posing a challenge to understand how each model affects the classificatio…
A new method selects the best ensemble for concept drift detection.
problem Concept drift detection in data streams.
method Dynamic ensemble selection focusing on decisionspace.
result Highest detection precision and lowest false alarms.
Generative model combines deep neural networks and ensemble methods for patient trajectory classification.
problem Classifying patient trajectories with scarce longitudinal data.
method Tensor factorization using deep recurrent neural networks and ensemble strategies.
result Achieved AUC over 0.85 for in-hospital mortality prediction.
Revises Bayesian model averaging for foundation models.
problem Ensemble pre-trained and lightly-finetuned foundation models for improved classification performance.
method Introduces trainable linear classifiers and computationally cheaper model averaging scheme (OMA).
result Ensembled models can better predict on various datasets.
RaSE ensemble framework improves sparse classification accuracy.
problem Sparse classification challenges in high-dimensional data.
method Random Subspace Ensemble (RaSE) framework with subspace selection via RIC.
result RaSE achieves low misclassification rates and accurate feature ranking.
Quantum algorithm improves ensemble classification with reduced memory and time requirements.
problem High memory and computational time requirements in ensemble methods.
method Quantum superposition, entanglement, and interference to build an ensemble of classification models.
result Exponential growth of ensemble size with linear increase in depth of circuit.
Ensemble CNNs improve mode classification in smartphone travel surveys.
problem Classifying transportation modes from smartphone travel survey data.
method Developed an ensemble of CNN models with different architectures and hyper-parameters, combined using average voting, majority voting, optimal weights, and a Random Forest meta-learner.
result The ensemble method with Random Forest as meta-learner achieved 91.8% accuracy, surpassing other methods.
It is common that a trained classification model is applied to the operating data that is deviated from the training data because of noise. This paper demonstrates that an ensemble classifier, Diversified Multiple Tree (DMT), is more robust in classifying noisy data than other widely used ensemble methods. DMT is teste…
Improved scalability of BOSS ensemble for time series classification.
problem Non-trivial scalability issues in BOSS and WEASEL.
method Random selection of classifiers, ensembling techniques, and build time reduction.
result Significant reduction in build time with minimal accuracy loss.
Classifier ensembles are pattern recognition structures composed of a set of classification algorithms (members), organized in a parallel way, and a combination method with the aim of increasing the classification accuracy of a classification system. In this study, we investigate the application of a generalized mixtur…
Differentially private ensemble classifiers adapt to data streams while protecting privacy.
problem Adapting to evolving data characteristics while protecting private information.
method Unbounded ensemble updates, model agnostic approach.
result Outperforms competitors on various privacy, drift, and distribution settings.
DEBAL uses ensemble methods to improve deep Bayesian active learning in image classification.
problem Mode collapse issue in Monte Carlo dropout for deep CNNs.
method Proposes DEBAL, an ensemble-based active learning strategy for deep neural networks.
result DEBAL improves deep Bayesian active learning, capturing superior data uncertainty and faster convergence.
Improved ROCKET algorithm for brain activity classification.
problem Classifying multivariate time series data from brain activity.
method Detach-Rocket Ensemble, leveraging pruning and ensemble methods.
result Competitive classification accuracy and interpretable channel relevance.
Adaptive XGBoost improves accuracy on evolving data streams by updating the ensemble dynamically.
problem Concept drift in evolving data streams.
method Adapts XGB to update the ensemble with new data, maintaining consistency with current concept.
result Improves classification accuracy on evolving data streams compared to other methods.
This work studies a unified approach to ensemble aggregation using likelihood perspective.
problem Density aggregation in machine learning, focusing on improving ensemble predictions.
method Normalized generalized mean of order r in the log-likelihood framework.
result The optimal range for r is [0,1], providing a principled justification for linear and geometric pooling.