A review of microarray datasets and applied feature selection methods
V Bolón-Canedo, N Sánchez-Marono… - Information …, 2014 - Elsevier
Microarray data classification is a difficult challenge for machine learning researchers due to
its high number of features and the small sample sizes. Feature selection has been soon …
its high number of features and the small sample sizes. Feature selection has been soon …
[HTML][HTML] Microarray cancer feature selection: Review, challenges and research directions
Microarray technology has become an emerging trend in the domain of genetic research in
which many researchers employ to study and investigate the levels of genes' expression in a …
which many researchers employ to study and investigate the levels of genes' expression in a …
Online feature selection with streaming features
We propose a new online feature selection framework for applications with streaming
features where the knowledge of the full feature space is unknown in advance. We define …
features where the knowledge of the full feature space is unknown in advance. We define …
A convex formulation for semi-supervised multi-label feature selection
Explosive growth of multimedia data has brought challenge of how to efficiently browse,
retrieve and organize these data. Under this circumstance, different approaches have been …
retrieve and organize these data. Under this circumstance, different approaches have been …
The severity prediction of the binary and multi-class cardiovascular disease− A machine learning-based fusion approach
In today's world, a massive amount of data is available in almost every sector. This data has
become an asset as we can use this enormous amount of data to find information. Mainly …
become an asset as we can use this enormous amount of data to find information. Mainly …
Feature selection with harmony search
Many search strategies have been exploited for the task of feature selection (FS), in an effort
to identify more compact and better quality subsets. Such work typically involves the use of …
to identify more compact and better quality subsets. Such work typically involves the use of …
[HTML][HTML] Computer-aided decision-making for predicting liver disease using PSO-based optimized SVM with feature selection
Using medical data mining models has been considered as a significant way to predict
diseases in recent years. In the field of healthcare, we face a large amount of data, and this …
diseases in recent years. In the field of healthcare, we face a large amount of data, and this …
[HTML][HTML] Identification of gene expression signature for drought stress response in barley (Hordeum vulgare L.) using machine learning approach
Barley (Hordeum vulgare L.) is an important cereal crop, playing a pivotal role in global
agriculture and food systems. Drought has a significant impact on barley growth and yield …
agriculture and food systems. Drought has a significant impact on barley growth and yield …
Feature selection inspired classifier ensemble reduction
Classifier ensembles constitute one of the main research directions in machine learning and
data mining. The use of multiple classifiers generally allows better predictive performance …
data mining. The use of multiple classifiers generally allows better predictive performance …
Selection of features for patient-independent detection of seizure events using scalp EEG signals
Epilepsy involves brain abnormalities that may cause sudden seizures or other
uncontrollable body activities. Epilepsy may have substantial impacts on the patient's quality …
uncontrollable body activities. Epilepsy may have substantial impacts on the patient's quality …