Multi-label classification algorithms based on supervised learning use all the labeled data to
train classifiers. However, in real life, many of the data are unlabeled, and it is costly to label …

Ensemble learning has been shown to be an effective approach to solve multi-label
classification problem. However, most existing ensemble learning methods do not consider …

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

Multilabel feature selection (MFS) has received widespread attention in various big data
applications. However, most of the existing methods either explicitly or implicitly assume that …

The class-imbalance is intrinsic in Multi-label datasets due to the higher number of labels,
few relevant labels in many instances, and a varied number of relevant instances for …

Multiobjective optimization problems are ubiquitous in real-world science, engineering, and
design optimization problems. It is not uncommon that the objective functions are as a black …

In multi-label classification, the expansion of output dimension seriously interferes learning
performance, and even fails to build a joint prediction model. In order to restrain the …

Multi-label algorithms often use an identical feature space to build classification models for
all labels. However, labels generally express different semantic information and should have …

Many real-world problems are computationally costly and the objective functions evolve over
time. Data-driven, aka surrogate-assisted, evolutionary optimization has been recognized as …

In multilabel learning, research on label correlation provides an effective solution to
compress the hypothesis space of classifiers. However, this article focus on the label …