A review of feature selection and its methods
B Venkatesh, J Anuradha - Cybernetics and information technologies, 2019 - sciendo.com
Nowadays, being in digital era the data generated by various applications are increasing
drastically both row-wise and column wise; this creates a bottleneck for analytics and also …
drastically both row-wise and column wise; this creates a bottleneck for analytics and also …
A comprehensive review of artificial intelligence-based approaches for rolling element bearing PHM: Shallow and deep learning
The objective of this paper is to present a comprehensive review of the contemporary
techniques for fault detection, diagnosis, and prognosis of rolling element bearings (REBs) …
techniques for fault detection, diagnosis, and prognosis of rolling element bearings (REBs) …
Temporally constrained sparse group spatial patterns for motor imagery BCI
Common spatial pattern (CSP)-based spatial filtering has been most popularly applied to
electroencephalogram (EEG) feature extraction for motor imagery (MI) classification in brain …
electroencephalogram (EEG) feature extraction for motor imagery (MI) classification in brain …
Bi-level semantic representation analysis for multimedia event detection
Multimedia event detection has been one of the major endeavors in video event analysis. A
variety of approaches have been proposed recently to tackle this problem. Among others …
variety of approaches have been proposed recently to tackle this problem. Among others …
Hyperspectral image classification via multitask joint sparse representation and stepwise MRF optimization
Hyperspectral image (HSI) classification is a crucial issue in remote sensing. Accurate
classification benefits a large number of applications such as land use analysis and marine …
classification benefits a large number of applications such as land use analysis and marine …
Review of classical dimensionality reduction and sample selection methods for large-scale data processing
X Xu, T Liang, J Zhu, D Zheng, T Sun - Neurocomputing, 2019 - Elsevier
In the era of big data, all types of data with increasing samples and high-dimensional
attributes are demonstrating their important roles in various fields, such as data mining …
attributes are demonstrating their important roles in various fields, such as data mining …
Assessing PD-L1 expression level by radiomic features from PET/CT in nonsmall cell lung cancer patients: an initial result
M Jiang, D Sun, Y Guo, Y Guo, J Xiao, L Wang… - Academic radiology, 2020 - Elsevier
Rationale and Objectives To explore the potential value of radiomic features-derived
approach in assessing PD-L1 expression status in nonsmall cell lung cancer (NSCLC) …
approach in assessing PD-L1 expression status in nonsmall cell lung cancer (NSCLC) …
Joint feature-sample selection and robust diagnosis of Parkinson's disease from MRI data
Parkinson's disease (PD) is an overwhelming neurodegenerative disorder caused by
deterioration of a neurotransmitter, known as dopamine. Lack of this chemical messenger …
deterioration of a neurotransmitter, known as dopamine. Lack of this chemical messenger …
Spatiotemporal-filtering-based channel selection for single-trial EEG classification
Achieving high classification performance in electroencephalogram (EEG)-based brain-
computer interfaces (BCIs) often entails a large number of channels, which impedes their …
computer interfaces (BCIs) often entails a large number of channels, which impedes their …
Sparse graph embedding unsupervised feature selection
High dimensionality is quite commonly encountered in data mining problems, and hence
dimensionality reduction becomes an important task in order to improve the efficiency of …
dimensionality reduction becomes an important task in order to improve the efficiency of …