Article history: Available online 20 November 2013

In this paper an approach to multi-class (as opposed to multi-label) classification is proposed. The idea is that a more effective classification can be produced if a coarse-grain classification (directed at groups of classes) is first conducted followed by increasingly more fine-grain classifications. A framework is proposed whereby this scheme can be realised in the form of a classification h...

1 Label Propagation for Clustering 1 Lovro Šubelj 1.1 Label Propagation Method 1 1.1.1 Resolution of Label Ties 3 1.1.2 Order of Label Propagation 3 1.1.3 Label Equilibrium Criterium 4 1.1.4 Algorithm and Complexity 5 1.2 Label Propagation as Optimization 6 1.3 Advances of Label Propagation 7 1.3.1 Label Propagation under Constraints 8 1.3.2 Label Propagation with Preferences 10 1.3.3 Method St...

Multi-label text classification (MLTC) involves tagging a document with its most relevant subset of labels from label set. In real applications, usually follow long-tailed distribution, where (called as tail-label) only contain small number documents and limit the performance MLTC. To facilitate this low-resource problem, researchers introduced simple but effective strategy, data augmentation (...

Multi-label text classification (MLTC) aims to annotate documents with the most relevant labels from a number of candidate labels. In real applications, distribution label frequency often exhibits long tail, i.e., few are associated large (a.k.a. head labels), while fraction small tail labels). To address challenge insufficient training data on classification, we propose Head-to-Tail Network (H...

Data representation is of significant importance in minimizing multi-label ambiguity. While most researchers intensively investigate label correlation, the research on enhancing model robustness preliminary. Low-quality data one main reasons that degrades. Aiming at cases with noisy features and missing labels, we develop a novel method called robust global local correlation (RGLC). In this mod...

We demonstrate an expanded procedure for assessing drug-label comprehension. Innovations include a pretest of drug preconceptions, verbal ability and label attentiveness measures, a label-scanning task, a free-recall test, category-clustering measures, and preconception-change scores. In total, 55 female and 39 male undergraduates read a facsimile Drug Facts Label for aspirin, a Cohesive-Prose ...