In multi-label learning, objects are essentially related to multiple semantic meanings, and
the type of data is confronted with the impact of high feature dimensionality simultaneously …

With the rapid advancement of big data technology, high-dimensional datasets comprising
multi-label data have become prevalent in various fields. However, these datasets often …

Class imbalance is a common issue in the community of machine learning and data mining.
The class-imbalance distribution can make most classical classification algorithms neglect …

Multi-label learning tasks usually encounter the problem of the class-imbalance, where
samples and their corresponding labels are non-uniformly distributed over multi-label data …

Neighborhood rough set based online streaming feature selection methods have aroused
wide concern in recent years and played a vital role in processing high-dimensional data …

Hospital readmission is one of the most important service quality measures. Recently,
numerous risk assessment models have been proposed to address the hospital readmission …

Background Nowadays, intelligent medicine is gaining widespread attention, and great
progress has been made in Western medicine with the help of artificial intelligence to assist …

Predicting 30-day hospital readmission is a core research task in the development of
personalized healthcare. However, the imbalanced class distribution and the heterogeneity …

In the era of Big Data, a practical yet challenging task is to make learning techniques more
universally applicable in dealing with the complex learning problem, such as multi-source …

Text multi-label learning deals with examples having multiple labels simultaneously. It can
be applied to many fields, such as text categorization, medical diagnosis recognition and …