ITCA optimizes label combination for ambiguous outcomes in multi-class classification.
problem Ambiguous outcome labels in real-world datasets hinder accurate multi-class classification.
method Information-theoretic classification accuracy (ITCA) and search strategies (greedy, breadth-first) guide label combination.
result ITCA improves prediction accuracy and identifies ambiguous labels across diverse applications.
New method for robust trajectory classification without parameters.
problem Trajectory classification accuracy and robustness.
method Parameter-free approach to find best trajectory partition and dimension combination.
result Promising preliminary results show improved robustness.
Combines human and model predictions for improved accuracy.
problem Improving classification accuracy when both human and model predictions are imperfect.
method Uses confusion matrices and calibration to combine probabilistic model outputs with human class-level predictions.
result Human-model combinations consistently outperform either alone, with accuracy gains even with limited human input.
New GM functions improve classifier ensemble accuracy.
problem Improving classifier ensemble accuracy.
method Using generalized mixture functions with dynamic weights.
result Gains in performance compared to traditional methods.
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.
Combines BERT and graph CNN for improved text classification.
problem Text classification problems
method Combining BERT embedding and graph convolutional neural network
result Graph CNN model performs better than classical models combined with BERT
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.
Paper proposes tensor sparse PCA for improved face recognition accuracy.
problem Face recognition accuracy improvement using novel methods.
method Combines tensor sparse PCA with nearest-neighbor and kernel ridge regression methods.
result Tensor sparse PCA method yields better accuracy than PCA method alone.
CosML combines domain-specific meta-learners for cross-domain few-shot classification.
problem Generalizing to unseen domains while meta-learning on multiple seen domains.
method CosML trains domain-specific meta-learners and combines their meta-parameters in the parameter space.
result CosML outperforms state-of-the-art methods and achieves strong cross-domain generalization.
Combines CNN and RNN for hierarchical image classification.
problem Hierarchical relations between image categories are not captured by flat classifiers.
method Uses a CNN for feature extraction and an RNN for capturing hierarchical class relations. Incorporates residual learning.
result Hierarchical networks outperform state-of-the-art CNNs on a real-world dataset.
Combines k-NN and RVM for improved classification accuracy.
problem Improving k-NN's performance by considering relevancy.
method Integrates k-NN and RVM in kernel space, introduces a new stopping parameter.
result Significantly prunes irrelevant attributes and improves classification accuracy.
Three methods combine one-class classifiers with MST-CD and N-ary Trees for binary classification.
problem Binary classification with overlapping and imbalanced classes.
method Combining one-class classifiers with MST-CD and N-ary Trees to handle inconsistencies and spurious connections.
result The proposed methods are feasible and comparable to state-of-the-art algorithms.
A new neural network model improves fMRI classification.
problem Classifying brain states using fMRI data.
method Developed a connectome-convolutional neural network (CCNN) for fMRI functional connectivity classification.
result CCNN outperforms single metric classifiers and can adapt to various connectivity descriptors.
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.
Random Projection (RP) technique has been widely applied in many scenarios because it can reduce high-dimensional features into low-dimensional space within short time and meet the need of real-time analysis of massive data. There is an urgent need of dimensionality reduction with fast increase of big genomics data. Ho…
Combines static and dynamic features for better multivariate sequence classification.
problem Improving classification performance using both static and dynamic features.
method Generative models (HMM and LSTM) extract temporal information from dynamic data. Static features are combined with the extracted temporal information.
result Hybrid approach outperforms other methods on public datasets.
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.
A novel weighted feature selection method using fuzzy sets improves classification accuracy and stability.
problem Improving feature selection accuracy and stability in machine learning models.
method Combination of four feature selection methods using fuzzy sets and bootstrap.
result Our method achieved significantly higher stability than individual methods.
A new method combines topological features with graph convolutional networks for improved paper classification.
problem Classifying papers based on their content and structure.
method Combining topological features of nodes with information propagation through Graph Convolutional Networks (GCN).
result The method improves classification accuracy on CiteSeer and Cora datasets, matching or exceeding text-based classification results.
Proposes a new technique for handling imbalanced data.
problem Data imbalance in classification tasks.
method Combines oversampling and undersampling techniques.
result CSMOUTE shows promise for handling imbalanced datasets.
This paper improves multi-label classification of user reactions in online news.
problem Classifying user reactions in online news with multi-label classification.
method Problem transformation methods combined with different classification algorithms.
result Classifier Chains method combined with Random Forest achieved the highest correct predictions.
This paper covers the two approaches for sentiment analysis: i) lexicon based method; ii) machine learning method. We describe several techniques to implement these approaches and discuss how they can be adopted for sentiment classification of Twitter messages. We present a comparative study of different lexicon combin…
Enhances sparse coding for motion data classification.
problem Efficiently decompose motion data into sparse combinations.
method Combines DTW and kernelized sparse coding with non-negative constraints.
result Effective in motion capture data interpretation and discrimination.
In the era of big data, a large amount of noisy and incomplete data can be collected from multiple sources for prediction tasks. Combining multiple models or data sources helps to counteract the effects of low data quality and the bias of any single model or data source, and thus can improve the robustness and the perf…
LNEMLC embeds label network for multi-label classification.
problem Lack of effective adaptation and preservation of generalization abilities for unseen label combinations.
method LNEMLC embeds label network to extend input space for any base multi-label classifier.
result Statistically significant improvements over simple kNN baseline classifier.
Combines multi-task and semi-supervised learning for disparate label spaces.
problem Sequence classification tasks with multiple, unrelated label sets.
method Joint embedding space and transfer functions between label embeddings.
result Outperforms strong baselines in topic-based sentiment analysis.
New online learning algorithm combines PA and TER for binary classification.
problem Binary classification with non-separable data and data imbalance.
method Online Passive-Aggressive (PA) and Total-Error-Rate (TER) learning combined into PATER algorithm.
result PATER algorithms outperform existing online learning algorithms in efficiency and effectiveness.
A framework for detecting out-of-distribution data in RL using uncertainty-based classification.
problem Detecting out-of-distribution data in deep reinforcement learning systems.
method A one-class classification problem approach based on epistemic uncertainty reduction.
result The proposed UBOOD framework reliably detects out-of-distribution situations when combined with ensemble-based uncertainty estimators.
Combines active learning and imbalance-aware classification for protein function prediction.
problem Scarce positive labels and lack of explicit negative labels in supervised learning.
method Active learning for selecting negative examples and imbalance-aware classification for mitigating label imbalance.
result The combined techniques outperform state-of-the-art methods on protein function prediction benchmarks.
Combines BERT and GCN for better text classification.
problem Limited global information capture by BERT.
method Integrates BERT with VGCN for improved text classification.
result VGCN-BERT outperforms BERT and GCN alone.
Unified framework DDNs for multi-label classification, improving inference efficiency.
problem Efficient inference for multi-label classification with dependency networks.
method Combining dependency networks and deep learning, proposing novel inference schemes.
result Novel inference schemes outperform basic neural architectures and Markov networks.
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.
Proposes adaptive method for classifying interval-valued time series.
problem Lack of classification methods for interval-valued time series.
method Represent intervals as images, classify using CNN, optimize coefficients with ADMM.
result Validated through simulations and real data, outperforming point-valued methods.
A new method for multi-label image classification using multiple feature views.
problem Limited by single-view feature, traditional matrix completion struggles with multi-label image classification.
method Multi-View Matrix Completion (MVMC) framework, combining weighted MC outputs from different views, using cross-validation for weights.
result MVMC framework improves multi-label image classification by exploiting complementary properties of different features and consistent labels.
We combine supervised learning with unsupervised learning in deep neural networks. The proposed model is trained to simultaneously minimize the sum of supervised and unsupervised cost functions by backpropagation, avoiding the need for layer-wise pre-training. Our work builds on the Ladder network proposed by Valpola (…
Paper improves differential privacy in sparse Gaussian process models.
problem Ensuring privacy in machine learning with sparse Gaussian processes.
method Combining differential privacy with sparse Gaussian processes, addressing low data density and high dimensions.
result Sparse approximation and modified Laplace approximation provide robust differential privacy in outlier areas and at higher dimensions.
Proposes a sparse linear classifier for classification with pairwise dependencies.
problem Classification accuracy is limited by tree-structured graphical models.
method Semi-parametric approach using sparse linear combination of univariate and bivariate log-transformed densities.
result SLB classifier is competitive with popular methods.
A novel deep learning technique combines multiple modalities, improving performance.
problem Challenges in leveraging different modalities due to noise and conflicts.
method Proposes a deep neural network that multiplicatively combines information from different modalities.
result Consistent accuracy improvements on three multimodal classification tasks.
A new method combines classification with population Monte Carlo for efficient ABC.
problem Inefficient particle proposals and subjectivity in ABC methods.
method Classification-PMC, blending adaptive proposals and classification.
result Classification-PMC outperforms state-of-the-art ABC methods in simulations.
Improved image classification accuracy on CIFAR-10 dataset.
problem Classifying images from the CIFAR-10 dataset with high accuracy.
method Combining features from manual and deep learning approaches, including VGG16, Inception ResNet v2, HOG, and pixel intensities.
result Achieved 94.6% testing accuracy by combining top 1000 principal components.
We complete the classification of rank two affine manifolds in the moduli space of translation surfaces in genus three. Combined with a recent result of Mirzakhani and Wright, this completes the classification of higher rank affine manifolds in genus three.
New method calibrates CNN-GP models for better uncertainty quantification.
problem Current CNN-GP models are miscalibrated, leading to unreliable uncertainty estimates.
method Proposes a novel combination of CNNs and GPs to improve calibration.
result Significantly outperforms previous approaches on calibration while maintaining state-of-the-art performance.
Improved object classification using neural networks with known and unknown features.
problem Improving classification accuracy for objects described by both known and unknown features.
method Modernized Informational Neurobayesian Approach with consideration of unknown features.
result The method completely solved the problem of misclassification for queries with combining known and unknown features.
Visual integration helps understand ensemble model performance.
problem Lack of comprehensibility in ensemble models.
method Visual integration of data and model space for effective exploration and manipulation of ensemble models.
result Improved understanding of how each model contributes to ensemble performance.
We present a general framework for classification of sparse and irregularly-sampled time series. The properties of such time series can result in substantial uncertainty about the values of the underlying temporal processes, while making the data difficult to deal with using standard classification methods that assume …
Proposes new fairness definitions for classification tasks combining statistical and individual fairness.
problem Combining statistical and individual fairness in classification tasks.
method Designs an oracle-efficient algorithm for fair empirical risk minimization.
result The ERM solution generalizes to new individuals and tasks.
Combining Bayesian deep learning and split conformal prediction affects out-of-distribution coverage.
problem Improving out-of-distribution coverage in multiclass image classification.
method Combining Bayesian deep learning with split conformal prediction methods.
result Combining methods can reduce out-of-distribution coverage in some cases.
EC3 combines clustering and classification for better ensemble learning performance.
problem Combining classification and clustering for improved prediction performance.
method EC3 merges classification and clustering using an optimization function and block coordinate descent.
result EC3 outperforms other methods by at most 10% in AUC on 13 benchmark datasets.