Feature selection: A data perspective
Feature selection, as a data preprocessing strategy, has been proven to be effective and
efficient in preparing data (especially high-dimensional data) for various data-mining and …
efficient in preparing data (especially high-dimensional data) for various data-mining and …
Machine learning and deep learning methods for intrusion detection systems: recent developments and challenges
G Kocher, G Kumar - Soft Computing, 2021 - Springer
Deep learning (DL) is gaining significant prevalence in every field of study due to its
domination in training large data sets. However, several applications are utilizing machine …
domination in training large data sets. However, several applications are utilizing machine …
Multiview clustering: A scalable and parameter-free bipartite graph fusion method
Multiview clustering partitions data into different groups according to their heterogeneous
features. Most existing methods degenerate the applicability of models due to their …
features. Most existing methods degenerate the applicability of models due to their …
A survey on deep learning for big data
Deep learning, as one of the most currently remarkable machine learning techniques, has
achieved great success in many applications such as image analysis, speech recognition …
achieved great success in many applications such as image analysis, speech recognition …
Group sparse regularization for deep neural networks
In this paper, we address the challenging task of simultaneously optimizing (i) the weights of
a neural network,(ii) the number of neurons for each hidden layer, and (iii) the subset of …
a neural network,(ii) the number of neurons for each hidden layer, and (iii) the subset of …
Recent advances in feature selection and its applications
Feature selection is one of the key problems for machine learning and data mining. In this
review paper, a brief historical background of the field is given, followed by a selection of …
review paper, a brief historical background of the field is given, followed by a selection of …
Incremental feature selection using a conditional entropy based on fuzzy dominance neighborhood rough sets
B Sang, H Chen, L Yang, T Li… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Incremental feature selection approaches can improve the efficiency of feature selection
used for dynamic datasets, which has attracted increasing research attention. Nevertheless …
used for dynamic datasets, which has attracted increasing research attention. Nevertheless …
Multi-modal physiological signals based squeeze-and-excitation network with domain adversarial learning for sleep staging
Sleep staging is the basis of sleep medicine for diagnosing psychiatric and
neurodegenerative diseases. However, the existing sleep staging methods ignore the fact …
neurodegenerative diseases. However, the existing sleep staging methods ignore the fact …
Feature selection for neural networks using group lasso regularization
We propose an embedded/integrated feature selection method based on neural networks
with Group Lasso penalty. Group Lasso regularization is considered to produce sparsity on …
with Group Lasso penalty. Group Lasso regularization is considered to produce sparsity on …
Diagnosing autism spectrum disorder from brain resting-state functional connectivity patterns using a deep neural network with a novel feature selection method
The whole-brain functional connectivity (FC) pattern obtained from resting-state functional
magnetic resonance imaging data are commonly applied to study neuropsychiatric …
magnetic resonance imaging data are commonly applied to study neuropsychiatric …