An adaptive neuro-fuzzy system with integrated feature selection and rule extraction for high-dimensional classification problems
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 …
dimensional datasets. This happens primarily due to the use of T-norm, particularly, product …
Unsupervised feature selection via adaptive autoencoder with redundancy control
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 …
unlabeled high-dimensional data. We present a novel adaptive autoencoder with …
Adaptive reverse graph learning for robust subspace learning
Subspace learning decreases the dimensions for high-dimensional data by projecting the
original data into a low-dimensional subspace, as well as preserving the similarity among …
original data into a low-dimensional subspace, as well as preserving the similarity among …
LESS-VFL: Communication-efficient feature selection for vertical federated learning
We propose LESS-VFL, a communication-efficient feature selection method for distributed
systems with vertically partitioned data. We consider a system of a server and several parties …
systems with vertically partitioned data. We consider a system of a server and several parties …
A smoothing group lasso based interval type-2 fuzzy neural network for simultaneous feature selection and system identification
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 open and close of the gate in the real world, is proposed to realize feature selection (FS) …
Adaptive type-2 fuzzy neural network inherited terminal sliding mode control for power quality improvement
S Hou, Y Chu, J Fei - IEEE transactions on industrial informatics, 2021 - ieeexplore.ieee.org
This article proposes an adaptive type-2 fuzzy neural network control system to enhance the
performance of power quality improvement. First, the dynamic model of APF with lumped …
performance of power quality improvement. First, the dynamic model of APF with lumped …
Surrogate-assisted and filter-based multiobjective evolutionary feature selection for deep learning
Feature selection (FS) for deep learning prediction models is a difficult topic for researchers
to tackle. Most of the approaches proposed in the literature consist of embedded methods …
to tackle. Most of the approaches proposed in the literature consist of embedded methods …
Long-term performance prediction of PEMFC based on LASSO-ESN
In recent years, with wide application of proton exchange membrane fuel cell (PEMFC) in
vehicles and portable applications, researches regarding PEMFC lifetime behavior and …
vehicles and portable applications, researches regarding PEMFC lifetime behavior and …
A joint multiobjective optimization of feature selection and classifier design for high-dimensional data classification
Feature selection (FS) in data mining and machine learning has attracted extensive
attention. The purpose of FS in a classification task is to find the optimal subset of features …
attention. The purpose of FS in a classification task is to find the optimal subset of features …
Dg-aletsk: a high-dimensional fuzzy approach with simultaneous feature selection and rule extraction
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 …
limitation in solving high-dimensional problems remains a challenging task. On the other …