Exabytes of data are generated daily by humans, leading to the growing needs for new
efforts in dealing with the grand challenges for multi-label learning brought by big data. For …

The drug development is generally arduous, costly, and success rates are low. Thus, the
identification of drug-target interactions (DTIs) has become a crucial step in early stages of …

In label-noise learning, the noise transition matrix, bridging the class posterior for noisy and
clean data, has been widely exploited to learn statistically consistent classifiers. The …

In multi-label learning, objects are essentially related to multiple semantic meanings, and
the type of data is confronted with the impact of high feature dimensionality simultaneously …

With the rapid advancement of big data technology, high-dimensional datasets comprising
multi-label data have become prevalent in various fields. However, these datasets often …

Multi-label classification is a practical yet challenging task in machine learning related fields,
since it requires the prediction of more than one label category for each input instance. We …

Information theoretical-based methods have attracted a great attention in recent years and
gained promising results for multilabel feature selection (MLFS). Nevertheless, most of the …

As we all know, multi-view data is more expressive than single-view data and multi-label
annotation enjoys richer supervision information than single-label, which makes multi-view …

Abstract Multi-Label Learning (MLL) aims to learn from the training data where each
example is represented by a single instance while associated with a set of candidate labels …

Multi-label learning (MLL) learns from the examples each associated with multiple labels
simultaneously, where the high cost of annotating all relevant labels for each training …