Partial label learning (PLL) is an emerging framework in weakly supervised machine
learning with broad application prospects. It handles the case in which each training …

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 …

Partial multi-label learning (PML) deals with problems where each instance is assigned with
a candidate label set, which contains multiple relevant labels and some noisy labels. Recent …

Partial multi-label learning refers to the problem that each instance is associated with a
candidate label set involving both relevant and noisy labels. Existing solutions mainly focus …

Class-conditional noise commonly exists in machine learning tasks, where the class label is
corrupted with a probability depending on its ground-truth. Many research efforts have been …

In partial multi-label learning (PML) problems, each instance is partially annotated with a
candidate label set, which consists of multiple relevant labels and some noisy labels. To …

In partial multi-label learning (PML), each data example is equipped with a candidate label
set, which consists of multiple ground-truth labels and other false-positive labels. Recently …

Counter-deception evidence fusion is a critical issue in the application of Dempster–Shafer
Theory (DST). Effectively detecting deceptive evidence poses a significant challenge in DST …

Partial multi-label (PML) learning refers to the modeling of prediction patterns from data
annotated with partially correct labels. Label embedding that finds a compact representation …

Partial multi-label learning (PML) aims at learning a robust multi-label classifier by training
on ambiguous data, where each sample is associated with a set of candidate labels, among …