Many existing researches employ one-vs-others approach to decompose a multi-label classification problem into a set of 2-class classification problems, one for each class. This approach is valid in traditional single-label classification. However, it incurs training inconsistency in multi-label classification, because a multi-label data point could belong to more than one class. In this work, w...

There has been a lot of research targeting text classification. Many of them focus on a particular characteristic of text data multi-labelity. This arises due to the fact that a document may be associated with multiple classes at the same time. The consequence of such a characteristic is the low performance of traditional binary or multi-class classification techniques on multi-label text data....

Many real-world applications require multi-label classification where multiple target labels are assigned to each instance. In multi-label classification, there exist the intrinsic correlations between the labels and features. These correlations are beneficial for multi-label classification task since they reflect the coexistence of the input and output spaces that can be exploited for predicti...

OBJECTIVE This research is motivated by the issue of classifying illnesses of chronically ill patients for decision support in clinical settings. Our main objective is to propose multi-label classification of multivariate time series contained in medical records of chronically ill patients, by means of quantization methods, such as bag of words (BoW), and multi-label classification algorithms. ...

The C-bound, introduced in Lacasse et al. [1], gives a tight upper bound on the risk of a binary majority vote classifier. In this work, we present a first step towards extending this work to more complex outputs, by providing generalizations of the C-bound to the multiclass and multi-label settings.

In standard matrix completion theory, it is required to have at least O(n ln n) observed entries to perfectly recover a low-rank matrix M of size n × n, leading to a large number of observations when n is large. In many real tasks, side information in addition to the observed entries is often available. In this work, we develop a novel theory of matrix completion that explicitly explore the sid...

Multi-label classification (MLC), which assigns multiple labels to each instance, is crucial domains from computer vision text mining. Conventional methods for MLC require huge amounts of labeled data capture complex dependencies between labels. However, such datasets are expensive, or even impossible, acquire. Worse yet, these pre-trained models can only be used the particular label set covere...