arXiv research

Advances rule-based multi-label classification using conformal prediction.

PML-GAN tackles noisy multi-label annotations using adversarial learning.

Graph-based multi-label classifier extends CULP for multi-label data.

C-HMCNN(h) improves HMC classification by leveraging class hierarchy.

Paper tackles gene mutation prediction for HCC using multi-instance multi-label learning.

Proposes LML layer for multi-label predictions with k labels.

The paper tackles multi-label ranking with uncertain probabilities.

Massively multi-label prediction/classification problems arise in environments like health-care or biology where very precise predictions are useful. One challenge with massively multi-label problems is that there is often a long-tailed frequency distribution for the labels, which results in few positive examples for t…

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…

Develops gradient boosting for multi-label classification.

Enhances medical code predictions for multi-morbidity patients using text classification.

Multi-label classification is an important learning problem with many applications. In this work, we propose a principled similarity-based approach for multi-label learning called SML. We also introduce a similarity-based approach for predicting the label set size. The experimental results demonstrate the effectiveness…

Two strategies extend multi-label chaining for imprecise probability estimates.

Dynamic classifier chains with XGBoost reduces multi-label classification costs and improves label dependency handling.

Proposes a new batch selection method for multi-label classification.

Extreme multi-label classification aims to learn a classifier that annotates an instance with a relevant subset of labels from an extremely large label set. Many existing solutions embed the label matrix to a low-dimensional linear subspace, or examine the relevance of a test instance to every label via a linear scan. …

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 …

FavMac maximizes value while controlling cost in multi-label prediction.

A new multi-label CPC method improves mutual information estimation and representation learning.

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…

The paper tackles skeptical binary inferences in multi-label problems with sets of probabilities.

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…

Class-imbalance is an inherent characteristic of multi-label data which affects the prediction accuracy of most multi-label learning methods. One efficient strategy to deal with this problem is to employ resampling techniques before training the classifier. Existing multilabel sampling methods alleviate the (global) im…

It is well-known that exploiting label correlations is crucially important to multi-label learning. Most of the existing approaches take label correlations as prior knowledge, which may not correctly characterize the real relationships among labels. Besides, label correlations are normally used to regularize the hypoth…

Proposes a self-paced multi-label learning method to handle diverse labels efficiently.

This paper develops convex surrogates for optimizing the multi-label F-measure.

Domain knowledge helps detect adversarial examples in multi-label classification.

This work explores how to balance rule consistency and coverage for multi-label classification.

A new method improves node classification in graphs with limited labels.

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…

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…

Paper tackles fault classification in time series data with deep neural networks.

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.

New framework learns from partial feedback in multi-label tasks.

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)…

Improves multi-label classification with a new network model.

DM2L tackles missing labels in multi-label learning by modeling local and global rank structures.

We consider multi-label classification where the goal is to annotate each data point with the most relevant $\textit{subset}$ of labels from an extremely large label set. Efficient annotation can be achieved with balanced tree predictors, i.e. trees with logarithmic-depth in the label complexity, whose leaves correspon…

Paper proposes a new MLC framework without predefined label order, improving performance and generalization.

Despite the success of ensemble classification methods in multi-class classification problems, ensemble methods based on approaches other than bagging have not been widely explored for multi-label classification problems. The Kalman Filter-based Heuristic Ensemble (KFHE) is an ensemble method that exploits the sensor f…

Efficiently discovers multi-label rules with relaxed pruning.

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…

MPVAE learns latent embeddings and label correlations for multi-label classification.

Multi-output prediction deals with the prediction of several targets of possibly diverse types. One way to address this problem is the so called problem transformation method. This method is often used in multi-label learning, but can also be used for multi-output prediction due to its generality and simplicity. In thi…

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 …

A new method for online multi-label stream classification.

pRSL combines probabilistic rules to improve multi-label classification.