• The local imbalance is more crucial than the global one in multi-label data. based measure assesses hardness of MLSOL and MLUL tackle class issue via imbalance. Suitable application situations our two methods are identified, respectively. Class an inherent characteristic data that hinders most learning methods. One efficient flexible strategy to deal with this problem employ sampling techniqu...

let $g=(v, e)$ be a graph. by emph{directional labeling (ord-labeling)} of an edge $x=uv$ of $g$ by an ordered $n$-tuple$(a_1,a_2,...,a_n)$, we mean a labeling of the edge $x$ such thatwe consider the label on $uv$ as $(a_1,a_2,...,a_n)$ in thedirection from $u$ to $v$, and the label on $x$ as$(a_{n},a_{n-1},...,a_1)$ in the direction from $v$ to $u$. inthis paper, we study graphs, called emph{...

A family of methods that generate soft labels by mixing the hard with a certain distribution, namely label refurbishment, are widely used to train deep neural networks. However, some these still poorly understood in presence noise. In this paper, we revisit four refurbishment and reveal strong connection between them. We find they affect network models different manners. Two them smooth estimat...

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...