In multi-label classification, where a single example may be associated with several class labels at the same time, ability to model dependencies between is considered crucial effectively optimize non-decomposable evaluation measures, such as Subset 0/1 loss. The gradient boosting framework provides well-studied foundation for learning models that are specifically tailored loss function and rec...

Multi-label classification refers to the task of predicting potentially multiple labels for a given instance. Conventional multi-label classification approaches focus on the single objective setting, where the learning algorithm optimizes over a single performance criterion (e.g. Ranking Loss) or a heuristic function. The basic assumption is that the optimization over one single objective can i...

With the diversification of the TC task, to construct multi-label classifier is often more in line with the needs of practical applications. However, in a multi-label classification task, each document often corresponds to more than one class label. In this chapter, we will construct a compound classification framework which may transform a multi-label classification task into several single la...

This paper presents a new experimental framework for studying multi-label evolving stream classification, with efficient methods that combine the best practices in streaming scenarios with the best practices in multi-label classification. Many real world problems involve data which can be considered as multi-label data streams. Efficient methods exist for multi-label classification in non strea...

Collective classification in relational data has become an important and active research topic in the last decade, where class labels for a group of linked instances are correlated and need to be predicted simultaneously. Collective classification has a wide variety of real world applications, e.g. hyperlinked document classification, social networks analysis and collaboration networks analysis...