A major limitation of fuzzy or neuro-fuzzy systems is their failure to deal with high-
dimensional datasets. This happens primarily due to the use of T-norm, particularly, product …

Unsupervised feature selection is one of the efficient approaches to reduce the dimension of
unlabeled high-dimensional data. We present a novel adaptive autoencoder with …

We propose a neural network-based feature selection (FS) scheme that can control the level
of redundancy in the selected features by integrating two penalties into a single objective …

Inspired by the life philosophy, an ingenious gate (membership) function, which can mimic
the open and close of the gate in the real world, is proposed to realize feature selection (FS) …

The problem of edge detection plays a crucial role in almost all research areas of image
processing. If edges are detected accurately, one can detect the location of objects and the …

Fuzzy or neuro-fuzzy systems have been successfully employed in many areas, but their
limitation in solving high-dimensional problems remains a challenging task. On the other …

Feature selection is an important issue in machine learning and data mining. Most existing
feature selection methods are greedy in nature thus are prone to sub-optimality. Though …

Objective The internal workings ofmachine learning algorithms are complex and considered
as low-interpretation" black box" models, making it difficult for domain experts to understand …

For a large number of network attacks, feature selection is used to improve intrusion
detection efficiency. A new mutual information algorithm of the redundant penalty between …

Feature selection is a crucial step in data processing and machine learning. While many
greedy and sequential feature selection approaches are available, a holistic neurodynamics …