Recently, multilabel classification has generated considerable research interest. However,
the high dimensionality of multilabel data incurs high costs; moreover, in many real …

The advent of high-throughput genomic technologies has resulted in the accumulation of
massive amounts of genomic information. However, biologists are challenged with how to …

Feature selection attempts to capture the more discriminative features and plays a significant
role in multilabel learning. As an efficient mathematical tool to handle incomplete and …

In multi-label classification, many existing works only pay attention to the label-specific
features and label correlation while they ignore the common features and instance …

Annotating multiple organs in medical images is both costly and time-consuming; therefore,
existing multi-organ datasets with labels are often low in sample size and mostly partially …

Existing multi-label learning approaches mainly utilize an identical data representation
composed of all the features in the discrimination of all the labels, and assume that all the …

In recent years, the means of disease diagnosis and treatment have been improved
remarkably, along with the continuous development of technology and science …

With the continuous development of artificial intelligence (AI) technology, big data-supported
AI technology with considerable computer and learning capacity has been applied in …

In multi-label learning, each sample is related to multiple labels simultaneously, and
attribute space of samples is with high-dimensionality. Therefore, the key issue for attribute …

Multi-label feature selection has grabbed intensive attention in many big data applications.
However, traditional multi-label feature selection methods generally ignore a real-world …