Multi-label learning studies the problem where each example is represented by a single
instance while associated with a set of labels simultaneously. During the past decade …

Multi‐label learning is quite a recent supervised learning paradigm. Owing to its capabilities
to improve performance in problems where a pattern may have more than one associated …

Currently, rapidly develo** digital technological innovations affect and change the
integrated information management processes of all sectors. The high efficiency of these …

Multi-label classification algorithms based on supervised learning use all the labeled data to
train classifiers. However, in real life, many of the data are unlabeled, and it is costly to label …

While deep learning succeeds in a wide range of tasks, it highly depends on the massive
collection of annotated data which is expensive and time-consuming. To lower the cost of …

Many social image search engines are based on keyword/tag matching. This is because tag-
based image retrieval (TBIR) is not only efficient but also effective. The performance of TBIR …

In graph-based semi-supervised learning approaches, the classification rate is highly
dependent on the size of the availabel labeled data, as well as the accuracy of the similarity …

In natural language processing community, sentiment classification based on insufficient
labeled data is a well-known challenging problem. In this paper, a novel semi-supervised …

Nonintrusive load monitoring refers to inferring what appliances are operating in a
household at a given time solely from fluctuations on the main power feeder. It is one …

Multi-label datasets often contain label information with missing values and recovering them
is a non-trivial challenge. Several methods augment the observed label matrix by …