In the past decades, the rapid growth of computer and database technologies has led to the
rapid growth of large-scale datasets. On the other hand, data mining applications with high …

Unsupervised feature selection attempts to select a small number of discriminative features
from original high-dimensional data and preserve the intrinsic data structure without using …

Feature selection (FS), which identifies the relevant features in a data set to facilitate
subsequent data analysis, is a fundamental problem in machine learning and has been …

Feature selection is a process aimed at filtering out unrepresentative features from a given
dataset, usually allowing the later data mining and analysis steps to produce better results …

The advent of high-throughput genomic technologies has resulted in the accumulation of
massive amounts of genomic information. However, biologists are challenged with how to …

Deep clustering aims to promote clustering tasks by combining deep learning and clustering
together to learn the clustering-oriented representation, and many approaches have shown …

Unsupervised attribute reduction becomes very challenging due to a lack of decision
information, which is to select a subset of attributes that can maintain learning ability without …

Multi-label feature selection has grabbed intensive attention in many big data applications.
However, traditional multi-label feature selection methods generally ignore a real-world …

With the continuous development of artificial intelligence (AI) technology, big data-supported
AI technology with considerable computer and learning capacity has been applied in …

The scale of the radius for constructing neighborhood relation has a great effect on the
results of neighborhood rough sets and corresponding measures. A very small radius …