Papers Multi-Label Classification

Gibbs Max-margin Topic Models with Data Augmentation

Max-margin learning is a powerful approach to building classifiers and structured output predictors. Recent work on max-margin supervised topic models has successfully integrated it with Bayesian topic models to discover…

Automatically Classifying Edit Categories in Wikipedia Revisions

Global Model for Hierarchical Multi-Label Text Classification

JRC EuroVoc Indexer JEX - A freely available multi-label categorisation tool

EuroVoc (2012) is a highly multilingual thesaurus consisting of over 6,700 hierarchically organised subject domains used by European Institutions and many authorities in Member States of the European Union (EU) for the c…

HYENA-live: Fine-Grained Online Entity Type Classification from Natural-language Text

Large-scale Multi-label Learning with Missing Labels

The multi-label classification problem has generated significant interest in recent years. However, existing approaches do not adequately address two key challenges: (a) the ability to tackle problems with a large number…

Ensemble Methods for Multi-label Classification

Ensemble methods have been shown to be an effective tool for solving multi-label classification tasks. In the RAndom k-labELsets (RAKEL) algorithm, each member of the ensemble is associated with a small randomly-selected…

Sexual predator detection in chats with chained classifiers

Multi-Label Classifier Chains for Bird Sound

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…

Dynamic Pruning of Factor Graphs for Maximum Marginal Prediction

We study the problem of maximum marginal prediction (MMP) in probabilistic graphical models, a task that occurs, for example, as the Bayes optimal decision rule under a Hamming loss. MMP is typically performed as a two-s…

Feature-aware Label Space Dimension Reduction for Multi-label Classification

Label space dimension reduction (LSDR) is an efficient and effective paradigm for multi-label classification with many classes. Existing approaches to LSDR, such as compressive sensing and principal label space transform…

Automatic Detection of Psychological Distress Indicators and Severity Assessment from Online Forum Posts

Multi-Target Regression via Input Space Expansion: Treating Targets as Inputs

In many practical applications of supervised learning the task involves the prediction of multiple target variables from a common set of input variables. When the prediction targets are binary the task is called multi-la…

Efficient Monte Carlo Methods for Multi-Dimensional Learning with Classifier Chains

Multi-dimensional classification (MDC) is the supervised learning problem where an instance is associated with multiple classes, rather than with a single class, as in traditional classification problems. Since these cla…

Maximum Margin Multi-Label Structured Prediction

We study multi-label prediction for structured output spaces, a problem that occurs, for example, in object detection in images, secondary structure prediction in computational biology, and graph matching with symmetries…

An Exact Algorithm for F-Measure Maximization

The F-measure, originally introduced in information retrieval, is nowadays routinely used as a performance metric for problems such as binary classification, multi-label classification, and structured output prediction. …

Reverse Multi-Label Learning

Multi-label classification is the task of predicting potentially multiple labels for a given instance. This is common in several applications such as image annotation, document classification and gene function prediction…