Feature selection is related to information processing, and its measurement and algorithm
use various intelligent methodologies, such as neighborhood rough sets (NRSs). At present …

The neighborhood rough set theory is a helpful instrument for working with data that is
numerical, and the performance of its uncertainty measures is generally stable. Even one …

Clustering, as an unsupervised data mining technique, allows us to classify similar objects
into the same cluster according to certain criteria. It helps us identify patterns between …

Multi-label feature selection plays an increasingly important role in alleviating the high
dimensionality of multi-label learning tasks. Most extant methods posit that the learning task …

Multi-source incomplete mixed data abound in real life, like medical data, biological data,
remote sensing data, military data, etc. However, some of these sources are of less …

In the era of open machine learning, a kind of data is accompanied by a hierarchical
structure between classes in the label space and the increasing number of features …

Feature selection is a crucial step for data preprocessing, and it is widely applied in machine
learning. It can eliminate features that are redundant or irrelevant from data, thereby …

Fuzzy rough set model is a powerful tool for handling attribute reduction tasks for complex
data. While the fuzzy rough set model commonly employs fuzzy information entropy to …

Due to limitations in data acquisition, data in real life often contains a wealth of uncertain
information. Uncertainty measurement (UM) constructed within the framework of rough set …

As information technology develops rapidly and data is constantly updated, efficient mining
knowledge from dynamic intuitionistic fuzzy information systems with fuzzy decision (IFFD) is …