Abstract. In multi-label learning, each sample is associated with several labels. Existing works indicate that exploring correlations between labels improve the prediction performance. However, embedding the label correlations into the training process significantly increases the problem size. Moreover, the mapping of the label structure in the feature space is not clear. In this paper, we prop...

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...

Text classification, the task of metadata to documents, requires significant time and effort when performed by humans. Moreover, with online-generated content explosively growing, it becomes a challenge for manually annotating with large scale and unstructured data. Currently, lots of state-or-art text mining methods have been applied to classification process, many of them based on the key wor...

Article history: Received 16 December 2014 Received in revised form 17 April 2015 Accepted 11 June 2015 Available online 23 June 2015

Multi-label classification is useful for text categorization, multimedia retrieval, and many other areas. A commonly used multi-label approach is the binary method, which constructs a decision function for each label. For some applications, adjusting thresholds in decision functions of the binary method significantly improves the performance, but few studies have been done on this subject. This...

Labeled data is often sparse in common learning scenarios, either because it is too time consuming or too expensive to obtain, while unlabeled data is almost always plentiful. This asymmetry is exacerbated in multi-label learning, where the labeling process is more complex than in the single label case. Although it is important to consider semisupervised methods for multi-label learning, as it ...