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 …

Multilabel learning has become a relevant learning paradigm in the past years due to the
increasing number of fields where it can be applied and also to the emerging number of …

文本自动分类是信息检索与数据挖掘领域的研究热点与核心技术, 近年来得到了广泛的关注和
快速的发展. 提出了基于机器学习的文本分类技术所面临的互联网内容信息处理等复杂应用的 …

A large body of research in supervised learning deals with the analysis of single-label data,
where training examples are associated with a single label λ from a set of disjoint labels L …

Multi-label feature selection is an efficient technique to deal with the high dimensional multi-
label data by selecting the optimal feature subset. Existing researches have demonstrated …

Multi-label learning deals with the problem where each example is represented by a single
instance (feature vector) while associated with a set of class labels. Existing approaches …

Relational learning is concerned with predicting unknown values of a relation, given a
database of entities and observed relations among entities. An example of relational …

Multi-label Learning is a form of supervised learning where the classification algorithm is
required to learn from a set of instances, each instance can belong to multiple classes and …

In multi-label learning, the training set is made up of instances each associated with a set of
labels, and the task is to predict the label sets of unseen instances. In this paper, this …

Multi-label learning deals with data belonging to different labels simultaneously. Like
traditional supervised feature selection, multi-label feature selection also plays an important …