Advances rule-based multi-label classification using conformal prediction.
problem Improving accuracy and decision making in multi-label classification.
method Combines conformal prediction with rule-based learning to provide natural conformity scores and calibrate rule assessments.
result Calibrated conformity scores enhance prediction accuracy and decision making.
PML-GAN tackles noisy multi-label annotations using adversarial learning.
problem Learning multi-label models from noisy, overcomplete annotations.
method PML-GAN uses a disambiguation network and a generative adversarial network to map noisy labels to clean labels and data samples.
result PML-GAN achieves state-of-the-art performance on partial multi-label learning datasets.
Graph-based multi-label classifier extends CULP for multi-label data.
problem Solving multi-label classification problems.
method Extends CULP algorithm to handle multi-label data.
result Competitive results compared to cutting-edge multi-label classifiers.
Novel approaches apply multi-label methods to sequential data tasks.
problem Applying multi-label methods to sequential data.
method Study connections between multi-label methods and Markovian models, develop novel approaches.
result Novel approaches outperform established methods in sequential prediction tasks.
C-HMCNN(h) improves HMC classification by leveraging class hierarchy.
problem Hierarchical multi-label classification with class hierarchy constraints.
method Exploits class hierarchy to produce coherent predictions for multi-label classification.
result C-HMCNN(h) outperforms state-of-the-art models in HMC classification.
Improved predictions for rare labels using neural networks and ontologies.
problem Long-tailed frequency distribution in multi-label prediction problems.
method Modified neural network output layer with a Bayesian network of sigmoids leveraging ontology relationships.
result Significant improvements in per-label AUROC and average precision for less common labels.
Paper tackles gene mutation prediction for HCC using multi-instance multi-label learning.
problem Gene mutation prediction in hepatocellular carcinoma for personalized treatments.
method Multi-instance multi-label learning with oversampling for data imbalance.
result Proposed approach shows superiority in gene mutation prediction.
Proposes LML layer for multi-label predictions with k labels.
problem Efficient multi-label prediction with limited labels.
method Probabilistic multi-label modeling, efficient forward and backward passes.
result Improves top-k recall and accuracy in multi-label tasks.
A new method learns label correlations for better multi-label predictions.
problem Label correlations not accurately characterized by existing approaches.
method Sparse reconstruction in the label space to learn correlations, then integrate into model training.
result Our approach outperforms state-of-the-art multi-label learning methods.
BP pretreatment reduces multi-label classification time.
problem Efficiently annotate large label sets for extreme multi-label classification.
method Divide instances into clusters, attach most relevant labels, train on pairs of clusters.
result BP reduces prediction time significantly without sacrificing accuracy.
SML improves multi-label classification accuracy.
problem Efficient multi-label classification with high accuracy.
method Similarity-based approach for multi-label learning and label set size prediction.
result SML outperforms existing algorithms across various evaluation criteria.
Convolutional residual model predicts diagnoses from EHR notes.
problem Predicting multiple diagnoses from medical text data.
method Convolutional Neural Network (CNN) + Deep Residual Network.
result Superior performance compared to baseline models.
The paper tackles multi-label ranking with uncertain probabilities.
problem Making skeptical inferences for multi-label ranking with sets of probabilities.
method Assumes a convex set of probabilities (credal set) over labels and seeks set-valued predictions.
result Developed methods for making skeptical inferences in multi-label ranking with uncertain probabilities.
Develops gradient boosting for multi-label classification.
problem Lack of customizable learning algorithms for multi-label classification.
method Generalizes gradient boosting to multi-output problems and proposes an algorithm for learning multi-label classification rules.
result Ability to minimize both decomposable and non-decomposable loss functions.
Enhances medical code predictions for multi-morbidity patients using text classification.
problem Improving accuracy in predicting medical codes for patients with multiple illnesses.
method Used machine learning techniques, including multi-label medical text classification, to enhance predictions.
result High dimensional embeddings pre-trained on health data significantly improve multi-label classification performance.
Two strategies extend multi-label chaining for imprecise probability estimates.
problem Handling imprecise probability estimates in multi-label classification.
method Adapting multi-label chaining to use convex sets of distributions (credal sets).
result Adapted approaches produce relevant cautiousness on hard-to-predict instances.
LdSM builds efficient multi-label decision trees with logarithmic depth.
problem Efficiently annotate data points with relevant subsets of labels from a large label set.
method Develops LdSM algorithm for multi-label decision trees with logarithmic depth, optimizing a novel objective function for balanced splits and high class purity.
result Minimizing the proposed objective function leads to pure and balanced data splits, achieving high prediction accuracy and low prediction time.
Dynamic classifier chains with XGBoost reduces multi-label classification costs and improves label dependency handling.
problem Static label ordering in multi-label classification limits model performance.
method Combining dynamic classifier chains with XGBoost for efficient multi-label prediction.
result Dynamic label ordering improves model performance and reduces training costs.
Online boosting algorithms improve multi-label ranking accuracy.
problem Improving multi-label ranking accuracy through online boosting.
method Design and analysis of online boosting algorithms with provable loss bounds.
result Our adaptive algorithm achieves comparable performance to existing batch boosting methods without requiring knowledge of weak learner edges.
Proposes a new batch selection method for multi-label classification.
problem Improving the accuracy of deep neural networks in multi-label classification tasks.
method Adapts uncertainty measures to multi-label data, considering label correlations and dynamic uncertainty.
result Improves performance and accelerates convergence of multi-label deep learning models.
New algorithm predicts multiple types of outputs with dependencies.
problem Predicting multiple diverse types of outputs.
method Problem transformation method combined with component-wise boosting.
result Sparse and interpretable learning of dependencies between targets.
NLDD predicts labelsets minimizing weighted distances in feature and label spaces.
problem Predicting labels independently ignores label correlations.
method NLDD minimizes a weighted sum of distances in feature and label spaces.
result NLDD outperforms other methods in multi-label classification metrics.
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.
FavMac maximizes value while controlling cost in multi-label prediction.
problem Value-maximizing predictions with strict cost control in multi-label scenarios.
method FavMac pipeline combining any multi-label classifier with online update mechanism.
result FavMac achieves higher value with strict cost control compared to baselines.
The paper introduces a method for multi-label classification that allows partial predictions.
problem Handling multi-label classification with the option to abstain from predictions.
method Formalized MLC with abstention as a generalized loss minimization problem.
result Initial results for Hamming loss, rank loss, and F-measure.
A new multi-label CPC method improves mutual information estimation and representation learning.
problem Underestimation of mutual information in contrastive predictive coding.
method Introducing a multi-label classification problem to overcome the logm bound in mutual information estimation. result The new method exceeds the logm bound and leads to better mutual information estimation and improved unsupervised representation learning. The paper tackles skeptical binary inferences in multi-label problems with sets of probabilities.
problem Making distributionally robust, skeptical inferences for multi-label problems.
method Study of distributionally robust, skeptical inferences for multi-label problems using Hamming loss.
result Skeptical inferences provide partial predictions for a sufficiently big set of probability distributions.
In this paper we study output coding for multi-label prediction. For a multi-label output coding to be discriminative, it is important that codewords for different label vectors are significantly different from each other. In the meantime, unlike in traditional coding theory, codewords in output coding are to be predic…
This paper develops convex surrogates for optimizing the multi-label F-measure.
problem Optimizing the F-measure for multi-label classification is computationally hard.
method Designing convex surrogate losses calibrated for the F-measure.
result The F-measure for multi-label problems has a rank of at most s2+1. Proposes a self-paced multi-label learning method to handle diverse labels efficiently.
problem Learning from multi-label data with a large label space is NP-hard and prone to overfitting.
method Self-paced multi-label learning with diversity (SPMLD) approach, incorporating gradual label inclusion and diversity maintenance.
result The proposed SPMLD framework optimizes a non-convex objective function using block coordinate descent.
Domain knowledge helps detect adversarial examples in multi-label classification.
problem Detecting adversarial examples in multi-label classification.
method Convert domain knowledge into constraints and inject them into a semi-supervised learning problem.
result Domain-knowledge constraints help detect adversarial examples effectively.
This work explores how to balance rule consistency and coverage for multi-label classification.
problem Balancing rule consistency and coverage for effective multi-label classification.
method Empirical study of rule learning heuristics in multi-label classification.
result The choice of heuristic for multi-label classification is crucial and depends on the performance measure.
A new method improves node classification in graphs with limited labels.
problem Semi-supervised multi-label node classification in attributed graphs.
method Collaborative Graph Walk (Multi-Label-Graph-Walk) using reinforcement learning.
result Significantly better multi-label classification performance compared to state-of-the-art methods.
Study proposes an ensemble learning method to improve multi-label classification performance.
problem Improving multi-label classification performance in machine learning.
method Ensemble learning approach using multiple base-level algorithms.
result Proposed method outperforms base-level algorithms in multi-label classification.
Improves multi-label text classification by regularizing model complexity and label dependencies.
problem High dimensional features and correlated labels in multi-label text classification.
method Regularizes model complexity using Elastic-net penalty and early stopping, and optimizes label search space with support inference and F-optimizer GFM.
result Significant improvement in accuracy on benchmark datasets, including unseen label combinations.
Multi-label classification has received considerable interest in recent years. Multi-label classifiers have to address many problems including: handling large-scale datasets with many instances and a large set of labels, compensating missing label assignments in the training set, considering correlations between labels…
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.
Advocates rule-based approach for multi-label classification.
problem Challenges in applying rule-based models to multi-label data.
method Rule learning algorithms for revealing patterns in multi-label data.
result Reveals new insights into multi-label data.
Multi-output inference tasks, such as multi-label classification, have become increasingly important in recent years. A popular method for multi-label classification is classifier chains, in which the predictions of individual classifiers are cascaded along a chain, thus taking into account inter-label dependencies and…
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.
Paper tackles fault classification in time series data with deep neural networks.
problem Fault classification over a future horizon in multidimensional time series data with class imbalances.
method Proposes a multi-label recurrent neural network with a new cost function to address class imbalances.
result The proposed algorithm outperforms state-of-the-art techniques in F1-score, precision, and recall.
We introduce structured prediction energy networks (SPENs), a flexible framework for structured prediction. A deep architecture is used to define an energy function of candidate labels, and then predictions are produced by using back-propagation to iteratively optimize the energy with respect to the labels. This deep a…
MCC algorithm predicts with partial modalities, outperforming full modalities.
problem Predicting with inconsistent and diverse multi-modal data.
method Instance-oriented Multi-modal Classifier Chains (MCC) algorithm.
result MCC outperforms full modalities in prediction.
New framework learns from partial feedback in multi-label tasks.
problem Learning from one-sided feedback in multi-label tasks.
method Probably Approximately Correct (PAC) framework for set functions.
result Achieves optimal sample complexity in realizable case, multiplicative approximation guarantees in agnostic case.
ML-Net improves multi-label text classification in biomedicine.
problem Challenges in multi-label text classification, especially in biomedicine.
method End-to-end deep learning framework combining label prediction and automated label count prediction.
result Significantly outperforms state-of-the-art methods in multi-label classification of biomedical texts.
Improves multi-label classification with a new network model.
problem Improving multi-label classification accuracy.
method Introduces Classifier Chain Network (CCN) for multi-label classification.
result CCN outperforms benchmark methods in simulations and real data.
DM2L tackles missing labels in multi-label learning by modeling local and global rank structures.
problem Missing labels in multi-label learning.
method DM2L imposes local low-rank structures and global high-rank structures on predictions of instances from the same and different labels, respectively.
result DM2L outperforms state-of-the-art methods in multi-label learning with missing labels.
Paper proposes a new MLC framework without predefined label order, improving performance and generalization.
problem Exposure bias in multi-label classification due to lack of predefined label order.
method Proposes a new framework that transforms MLC into a sequence prediction problem without predefined label order.
result The proposed method outperforms competitive baselines and has better generalization capability.