Mechanical intelligent fault diagnosis is an important method to accurately identify the health
status of mechanical equipment and ensure its safe operation. With the advent of the “big …

In recent years, deep neural networks have been successful in both industry and academia,
especially for computer vision tasks. The great success of deep learning is mainly due to its …

Label noise hampers supervised training of neural networks. However, data without label
noise is often infeasible to attain, especially for medical tasks. Attaining high-quality medical …

Feature reduction is an important aspect of Big Data analytics on today's ever-larger
datasets. Rough sets are a classical method widely applied in attribute reduction. Most …

This article presents a general sampling method, called granular-ball sampling (GBS), for
classification problems by introducing the idea of granular computing. The GBS method …

Learning representations for graphs plays a critical role in a wide spectrum of downstream
applications. In this paper, we summarize the limitations of the prior works in three folds …

Granular computing is an efficient and scalable computing method. Most of the existing
granular computing-based classifiers treat the granules as a preliminary feature procession …

Feature selection has aroused extensive attention and aims at selecting features that are
highly relevant to classification from raw datasets to improve the performance of a learning …

The monitoring and tracking of construction equipment, eg, excavators, is of great interest to
improve the productivity, safety, and sustainability of construction projects. In recent years …

Since minority samples are substantially less common than majority samples, many
industrial applications, such as credit card fraud detection (CCFD) and defective part …