• 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...

Multi-label learning has attracted significant attention from machine learning and data mining over the last decade. Although many multi-label classification algorithms have been devised, few research studies focus on multi-assignment clustering (MAC), in which a data instance can be assigned to multiple clusters. The MAC problem is practical in many application domains, such as document cluste...

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...

ML-kNN is a well-known algorithm for multi-label classification. Although effective in some cases, ML-kNN has some defect due to the fact that it is a binary relevance classifier which only considers one label every time. In this paper, we present a new method for multi-label classification, which is based on lazy learning approaches to classify an unseen instance on the basis of its k nearest ...

Multi-label classification aims to assign multiple labels to a single test instance. Recently, more and more multi-label classification applications arise as large-scale problems, where the numbers of instances, features and labels are either or all large. To tackle such problems, in this paper we develop a clustering-based local multi-label classification method, attempting to reduce the probl...

Partial multi-label learning (PML), which tackles the problem of prediction models from instances with overcomplete noisy annotations, has recently started gaining attention research community. In this paper, we propose a novel adversarial model, PML-GAN, under generalized encoder-decoder framework for partial learning. The PML-GAN model uses disambiguation network to identify irrelevant labels...