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.
A method to generate multi-label data from single positive annotations.
problem Generating multi-label datasets is costly and impractical.
method Single-to-multi-label (S2M) sampling using Markov chain Monte Carlo.
result S2M sampling enables high-quality multi-label data with minimal annotation cost.
Proposes methods to improve multi-label learning by addressing local label imbalance.
problem Local label imbalance within minority class examples degrades multi-label learning performance.
method Introduces a measure to assess local label imbalance and two sampling approaches (MLSOL, MLUL) to address it.
result Experimental results show MLSOL and MLUL improve performance on multi-label datasets.
Proposes a data augmentation method to improve multi-label learning performance.
problem Improving multi-label learning by exploiting label correlations and data augmentation.
method Proposes a novel data augmentation approach that performs clustering on real examples and treats cluster centers as virtual examples, promoting local smoothness through a regularization term.
result Extensive experiments show that the proposed method outperforms state-of-the-art multi-label learning approaches.
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.
A new method reduces noise in multi-label data and reduces dimensionality.
problem Handling noisy multi-label data in semi-supervised settings.
method Semi-supervised and multi-label dimensionality reduction method using label propagation.
result NMLSDR outperforms state-of-the-art algorithms in reducing noise and dimensionality.
A new LDA variant improves multi-label classification performance.
problem Improving multi-label classification performance.
method Saliency-based weights redefine between-class and within-class scatter matrices for multi-label classification.
result The proposed method leads to performance improvements in various multi-label classification problems.
Multi-label classification has attracted an increasing amount of attention in recent years. To this end, many algorithms have been developed to classify multi-label data in an effective manner. However, they usually do not consider the pairwise relations indicated by sample labels, which actually play important roles i…
Improves NILM with multi-label SRC, outperforming state-of-the-art.
problem Non-intrusive load monitoring (NILM) for energy disaggregation.
method Modified multi-label sparse representation based classification (SRC).
result Significant improvement over state-of-the-art techniques with minimal training data.
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.
Paper proposes scalable multi-label classification for edge devices using CNN.
problem Challenges in deploying multi-label CNN models on edge devices due to high computation and memory requirements.
method Extends existing multi-label classification methods with a single CNN model and multiple loss and accuracy layers.
result Achieves comparable accuracy with 1.8x less MACC operations, 0.97x reduction in latency and 0.5x, 0.84x, 0.97x reduction in size for generated CNN models.
Proposes MLPSVM for multi-label learning, improving on binary relevance.
problem Handles multi-label learning tasks more efficiently than binary relevance.
method Uses standard support vector machines with parallel decision hyper-planes.
result Outperforms other multi-label learning algorithms on various data sets.
A federated method for feature selection in multi-label data.
problem Feature selection in multi-label data for distributed and federated environments.
method Semi-Supervised Federated Multi-Label Feature Selection (SSFMLFS) using fuzzy information measures.
result SSFMLFS outperforms other methods in feature selection for multi-label data in federated settings.
Paper proposes a distributed algorithm for multi-label feature selection.
problem Maximizing diversity and quality in non-redundant feature selection.
method Greedy algorithm for distributed optimization of submodular plus diversity functions.
result Achieves constant factor approximation of optimal solution in big data settings.
The number of methods available for classification of multi-label data has increased rapidly over recent years, yet relatively few links have been made with the related task of classification of sequential data. If labels indices are considered as time indices, the problems can often be seen as equivalent. In this pape…
As data streams become more prevalent, the necessity for online algorithms that mine this transient and dynamic data becomes clearer. Multi-label data stream classification is a supervised learning problem where each instance in the data stream is classified into one or more pre-defined sets of labels. Many methods hav…
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.
Multi-label classification aims to classify instances with discrete non-exclusive labels. Most approaches on multi-label classification focus on effective adaptation or transformation of existing binary and multi-class learning approaches but fail in modelling the joint probability of labels or do not preserve generali…
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.
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.
Class imbalance is an intrinsic characteristic of multi-label data. Most of the labels in multi-label data sets are associated with a small number of training examples, much smaller compared to the size of the data set. Class imbalance poses a key challenge that plagues most multi-label learning methods. Ensemble of Cl…
We consider the multi-label ranking approach to multi-label learning. Boosting is a natural method for multi-label ranking as it aggregates weak predictions through majority votes, which can be directly used as scores to produce a ranking of the labels. We design online boosting algorithms with provable loss bounds for…
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.
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.
A new method for online multi-label stream classification.
problem Challenges in classifying continuous data streams with concept drift and delayed labels.
method Online unsupervised incremental method based on self-organizing maps.
result The method is highly competitive in both stationary and concept drift scenarios.
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.
Paper proposes a method to recover accurate labels from partially valid data in multi-label learning.
problem Tackles noisy supervision in multi-label learning with partially valid labels.
method Develops a two-stage method that estimates label enrichment and ground-truth confidences.
result Demonstrates improved performance over state-of-the-art PML methods.
Paper tackles interpretability in deep learning for multi-label learning.
problem Machine learning models are often hard to interpret.
method Combines deep autoencoder and multi-label classifiers.
result Proposes interpretable label hierarchies and dependencies.
Prototypical Networks improve multi-label classification accuracy.
problem Multi-label classification with nonlinear label dependencies.
method Formulate multi-label learning as class distribution in a non-linear embedding space. For each label, positive and negative embeddings are compactly distributed. Labels are inferred by measuring the distance to prototype positive or negative embeddings.
result Extensive experiments show improved accuracy compared to state-of-the-art algorithms.
Unified surrogate loss framework for multi-label learning with strong consistency guarantees.
problem Improving consistency and accounting for label correlations in multi-label learning.
method Introducing multi-label logistic loss and extending it to comprehensive multi-label comp-sum losses, proving strong consistency guarantees for any multi-label loss.
result Unified surrogate loss framework benefiting from strong consistency guarantees for any multi-label loss.
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.
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.
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.
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 …
Zero-shot learning transfers knowledge from seen classes to novel unseen classes to reduce human labor of labelling data for building new classifiers. Much effort on zero-shot learning however has focused on the standard multi-class setting, the more challenging multi-label zero-shot problem has received limited attent…
HMS-BERT detects cyberbullying in multiple languages and labels.
problem Multilingual and multi-label cyberbullying detection challenges.
method Hybrid multi-task self-training framework using BERT.
result Strong performance on multi-label and main classification tasks.
Labeled Latent Dirichlet Allocation (LLDA) is an extension of the standard unsupervised Latent Dirichlet Allocation (LDA) algorithm, to address multi-label learning tasks. Previous work has shown it to perform in par with other state-of-the-art multi-label methods. Nonetheless, with increasing label sets sizes LLDA enc…
FLDCRF improves sequence labeling performance with latent dynamics interactions.
problem Sequence labeling with improved performance and latent dynamics interactions.
method Factored Latent-Dynamic Conditional Random Fields (FLDCRF) with multiple latent dynamics interactions.
result FLDCRF outperforms state-of-the-art models across multiple datasets.
Proposes an online metric learning method for multi-label classification.
problem Lack of consideration for label dependencies and theoretical analysis of loss functions in existing multi-label classification methods.
method Develops a novel online metric learning paradigm based on k-Nearest Neighbour (kNN) and large margin principle, adapted for online streaming data.
result The proposed OML algorithm outperforms state-of-the-art methods on benchmark multi-label datasets.
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.
Multi-label classification (MLC) is a supervised learning problem in which, contrary to standard multiclass classification, an instance can be associated with several class labels simultaneously. In this chapter, we advocate a rule-based approach to multi-label classification. Rule learning algorithms are often employe…
Simplifies multi-label classification with stochastic sketch strategy.
problem Complex training processes in multi-label classification.
method Simple stochastic sketch strategy for multi-label classification.
result Competitive performance without complex training processes.
Multi-label classification is a type of supervised learning where an instance may belong to multiple labels simultaneously. Predicting each label independently has been criticized for not exploiting any correlation between labels. In this paper we propose a novel approach, Nearest Labelset using Double Distances (NLDD)…
Predicting diagnoses from Electronic Health Records (EHRs) is an important medical application of multi-label learning. We propose a convolutional residual model for multi-label classification from doctor notes in EHR data. A given patient may have multiple diagnoses, and therefore multi-label learning is required. We …
Bird sound data collected with unattended microphones for automatic surveys, or mobile devices for citizen science, typically contain multiple simultaneously vocalizing birds of different species. However, few works have considered the multi-label structure in birdsong. We propose to use an ensemble of classifier chain…
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.
Kernel alignment measures the degree of similarity between two kernels. In this paper, inspired from kernel alignment, we propose a new Linear Discriminant Analysis (LDA) formulation, kernel alignment LDA (kaLDA). We first define two kernels, data kernel and class indicator kernel. The problem is to find a subspace to …
Paper presents a multi-label topic model for financial texts with high performance and insights into market reactions.
problem Analyzing financial text data for market reactions and understanding topic interactions.
method Trained a multi-label topic model on a financial text database, achieved high macro F1 score, and investigated topic interactions.
result Model achieves high performance (macro F1 > 85%) and reveals significant market reactions to topic co-occurrences.