Feature selection facilitates uncertainty disposal and information mining, and it has received
widespread research interests. Divergence-based fuzzy rough sets (Div-FRSs), a new kind …

In this work different repositioning rules are investigated and compared in the context of free-
floating bike-sharing systems. A static complete reposition setting is adopted, ie, the system …

In the era of big data, both the size and the number of features, samples, and classes
continue to increase, resulting in high-dimensional classification tasks. One characteristic …

Fuzzy rough entropy established in the notion of fuzzy rough set theory, which has been
effectively and efficiently applied for feature selection to handle the uncertainty in real …

Few-shot learning plays an important role in the field of machine learning. Many existing
methods based on relation network achieve satisfactory results. However, these methods …

Due to advancement of internet and lab based technologies, large volume of high
dimensional data are generated every day. These data usually consisted of several issues …

Attribute reduction plays a critical role in extracting valuable information from high-
dimensional datasets. Compared to Pawlak rough set, fuzzy rough set can preserve more …

In the age of big data, the number of class labels is increasing rapidly and there exists a
hierarchical structure between different class labels. In the present paper, we revisit the …

For hierarchical classification tasks, label relationships can be represented as a hierarchical
structure ranging from coarse-grained to fine-grained. Existing hierarchical classifications …

There are no clear boundaries between internal tissues in sheep Computerized
Tomography images, and it is difficult for traditional methods to meet the requirements of …