A comprehensive survey on the process, methods, evaluation, and challenges of feature selection
Feature selection is employed to reduce the feature dimensions and computational
complexity by eliminating irrelevant and redundant features. A vast amount of increasing …
complexity by eliminating irrelevant and redundant features. A vast amount of increasing …
An intelligent healthcare monitoring framework using wearable sensors and social networking data
Wearable sensors and social networking platforms play a key role in providing a new
method to collect patient data for efficient healthcare monitoring. However, continuous …
method to collect patient data for efficient healthcare monitoring. However, continuous …
[PDF][PDF] Feature selection methods: Case of filter and wrapper approaches for maximising classification accuracy.
Feature selection has been widely applied in many areas such as classification of spam
emails, cancer cells, fraudulent claims, credit risk, text categorisation and DNA microarray …
emails, cancer cells, fraudulent claims, credit risk, text categorisation and DNA microarray …
DNS rule-based schema to botnet detection
Botnets are considered a serious issue today. They have several negative economic
impacts as well. Such impacts are affecting organizations and individuals. Recent botnets …
impacts as well. Such impacts are affecting organizations and individuals. Recent botnets …
A new hybrid intelligent system for accurate detection of Parkinson's disease
Elderly people are commonly affected by Parkinson's disease (PD) which is one of the most
common neurodegenerative disorders due to the loss of dopamine-producing brain cells …
common neurodegenerative disorders due to the loss of dopamine-producing brain cells …
A novel hybrid wrapper–filter approach based on genetic algorithm, particle swarm optimization for feature subset selection
The classification is one of the main technique of machine learning science. In many
problems, the data sets have a high dimensionality that the existence of all features is not …
problems, the data sets have a high dimensionality that the existence of all features is not …
[HTML][HTML] A scoping review of cerebral Doppler arterial waveforms in infants
Cerebral Doppler ultrasound has been an important tool in pediatric diagnostics and
prognostics for decades. Although the Doppler spectrum can provide detailed information on …
prognostics for decades. Although the Doppler spectrum can provide detailed information on …
Complex networks approach for EEG signal sleep stages classification
Sleep stage scoring is a challenging task. Most of existing sleep stage classification
approaches rely on analysing electroencephalography (EEG) signals in time or frequency …
approaches rely on analysing electroencephalography (EEG) signals in time or frequency …
A three-stage unsupervised dimension reduction method for text clustering
Dimension reduction is a well-known pre-processing step in the text clustering to remove
irrelevant, redundant and noisy features without sacrificing performance of the underlying …
irrelevant, redundant and noisy features without sacrificing performance of the underlying …
Investigate contribution of multi-microseismic data to rockburst risk prediction using support vector machine with genetic algorithm
B Ji, F Xie, X Wang, S He, D Song - Ieee Access, 2020 - ieeexplore.ieee.org
As a severe hazard in coal mining and rock excavation, the rockburst is usually induced by
the high energy tremor. Microseismic (MS) monitoring is suggested to forecast the rockburst …
the high energy tremor. Microseismic (MS) monitoring is suggested to forecast the rockburst …