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...

Multi-label feature selection has been widely adopted to address multi-label data with high-dimension features. It is critical calculate label correlations for selection. Existing methods adopt different schemes correlations, which obtain importance of labels. However, there exist two issues regarding these calculating importance: first, previous cannot predict the whole labels well because the...

In multi-label learning, each instance is associated with multiple labels simultaneously. Multi-label data often has noisy, irrelevant, and redundant features of high dimensionality. feature selection received considerable attention as an effective means for dealing high-dimensional data. Many methods exploit label correlations to help select features. However, finding selecting in existing are...

Feature selection has attracted considerable attention due to the wide application of multi-label learning. However, previous methods do not fully consider relationship between feature sets and label but devote either them. Furthermore, conventional learning utilizes logical labels estimate relevance so that importance corresponding cannot be well reflected. Additionally, numerous irrelevant re...

In many real data mining tasks, one data object is often associated with multiple class labels simultaneously; for example, a document may belong to multiple topics, an image can be tagged with multiple terms, etc. Multi-label learning focuses on such problems, and it is well accepted that the exploitation of relationship among labels is crucial; actually this is the essential difference betwee...