The problem of multi-label classification has attracted great interests in the last decade. Multi-label classification refers to the problems where an example that is represented by a single instance can be assigned tomore than one category. Until now, most of the researches on multi-label classification have focused on supervised settings whose assumption is that large amount of labeled traini...

Many real world problems involve data which can be considered as multi-label data streams. Efficient methods exist for multi-label classification in non streaming scenarios. However, learning in evolving streaming scenarios is more challenging, as the learners must be able to adapt to change using limited time and memory. This paper proposes a new experimental framework for studying multi-label...

Multi-label classification is the problem that classes are not mutually exclusive, so that an example may belong to more than one category. This poses challenges to the traditional pattern recognition theory where class overlap means classification error. Multi-label classification arises typically in semantic scene classification, text categorization, medical diagnosis, and bioinformatics. How...

The aim of this paper is to elaborate on the important issue of label dependence in multi-label classification (MLC). Looking at the problem from a statistical perspective, we claim that two different types of label dependence should be distinguished, namely conditional and unconditional. We formally explain the differences and connections between both types of dependence and illustrate them by...

Classifying text data has been an active area of research for a long time. Text document is multifaceted object and often inherently ambiguous by nature. Multi-label learning deals with such ambiguous object. Classification of such ambiguous text objects often makes task of classifier difficult while assigning relevant classes to input document. Traditional single label and multi class text cla...

In this paper a high speed neural network classifier based on extreme learning machines for multi-label classification problem is proposed and discussed. Multi-label classification is a superset of traditional binary and multiclass classification problems. The proposed work extends the extreme learning machine technique to adapt to the multi-label problems. As opposed to the singlelabel problem...

We propose a novel cost-sensitive multi-label classification algorithm called cost-sensitive random pair encoding (CSRPE). CSRPE reduces the costsensitive multi-label classification problem to many cost-sensitive binary classification problems through the label powerset approach followed by the classic oneversus-one decomposition. While such a näıve reduction results in exponentiallymany classi...

In traditional classification problems (single-label), patterns are usually associated with a single label from a set of two or more classes. When an example can simultaneously belong to more than one class (label), this classification problem is known as multi-label classification problem. Multi-label classification methods have been increasingly used in modern applications, such as music cate...

In this paper, a high-speed online neural network classifier based on extreme learning machines for multi-label classification is proposed. In multi-label classification, each of the input data sample belongs to one or more than one of the target labels. The traditional binary and multi-class classification where each sample belongs to only one target class forms the subset of multi-label class...

Department of Computer Science and Technology, Tongji University, Shanghai 201804, PR China Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2G7, Canada Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai 201804, PR China d System Research Institute, Polish Academy of Sciences, Warsaw, Poland e Sch...