A review of feature selection methods for machine learning-based disease risk prediction
N Pudjihartono, T Fadason, AW Kempa-Liehr… - Frontiers in …, 2022 - frontiersin.org
Machine learning has shown utility in detecting patterns within large, unstructured, and
complex datasets. One of the promising applications of machine learning is in precision …
complex datasets. One of the promising applications of machine learning is in precision …
A review of machine learning in processing remote sensing data for mineral exploration
The decline of the number of newly discovered mineral deposits and increase in demand for
different minerals in recent years has led exploration geologists to look for more efficient and …
different minerals in recent years has led exploration geologists to look for more efficient and …
A survey of ensemble learning: Concepts, algorithms, applications, and prospects
Ensemble learning techniques have achieved state-of-the-art performance in diverse
machine learning applications by combining the predictions from two or more base models …
machine learning applications by combining the predictions from two or more base models …
Measuring and computing cognitive statuses of construction workers based on electroencephalogram: a critical review
Construction workers' cognitive statuses affecting their safety and productivity are essential
for successful construction management. Electroencephalogram (EEG) provides a potential …
for successful construction management. Electroencephalogram (EEG) provides a potential …
Ensemble feature selection in medical datasets: Combining filter, wrapper, and embedded feature selection results
Feature selection is a process aimed at filtering out unrepresentative features from a given
dataset, usually allowing the later data mining and analysis steps to produce better results …
dataset, usually allowing the later data mining and analysis steps to produce better results …
Ensemble feature selection for high-dimensional data: a stability analysis across multiple domains
B Pes - Neural Computing and Applications, 2020 - Springer
Selecting a subset of relevant features is crucial to the analysis of high-dimensional datasets
coming from a number of application domains, such as biomedical data, document and …
coming from a number of application domains, such as biomedical data, document and …
A hybrid ensemble-filter wrapper feature selection approach for medical data classification
Background and objective Medical data plays a decisive role in disease diagnosis. The
classification accuracy of high-dimensional datasets is often diminished by several …
classification accuracy of high-dimensional datasets is often diminished by several …
Efficient and compact face descriptor for driver drowsiness detection
Current advances in driver drowsiness detection consist of a variety of innovative
technologies generally based on driver state monitoring systems. Extracting effective and …
technologies generally based on driver state monitoring systems. Extracting effective and …
A feature selection method based on multiple feature subsets extraction and result fusion for improving classification performance
J Liu, D Li, W Shan, S Liu - Applied Soft Computing, 2024 - Elsevier
Directly applying high-dimensional data to machine learning leads to dimensionality
disasters and may induce model overfitting. Feature selection can effectively reduce feature …
disasters and may induce model overfitting. Feature selection can effectively reduce feature …
Hierarchical voting-based feature selection and ensemble learning model scheme for glioma grading with clinical and molecular characteristics
Determining the aggressiveness of gliomas, termed grading, is a critical step toward
treatment optimization to increase the survival rate and decrease treatment toxicity for …
treatment optimization to increase the survival rate and decrease treatment toxicity for …