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 …
[HTML][HTML] Data-driven modelling for assessing trophic status in marine ecosystems using machine learning approaches
Assessing eutrophication in coastal and transitional waters is of utmost importance, yet
existing Trophic Status Index (TSI) models face challenges like multicollinearity, data …
existing Trophic Status Index (TSI) models face challenges like multicollinearity, data …
Particle guided metaheuristic algorithm for global optimization and feature selection problems
Optimization problems can be seen in numerous fields of practical studies. One area making
waves in the application of optimization methods is data mining in machine learning. An …
waves in the application of optimization methods is data mining in machine learning. An …
[HTML][HTML] Predicting agricultural drought indicators: ML approaches across wide-ranging climate and land use conditions
Agricultural drought can severely reduce crop yields, lead to large economic losses and
health impacts. Combined climate and land use variations determine key indicators of …
health impacts. Combined climate and land use variations determine key indicators of …
Densely connected neural networks for nonlinear regression
C Jiang, C Jiang, D Chen, F Hu - Entropy, 2022 - mdpi.com
Densely connected convolutional networks (DenseNet) behave well in image processing.
However, for regression tasks, convolutional DenseNet may lose essential information from …
However, for regression tasks, convolutional DenseNet may lose essential information from …
Development of real time ECG monitoring and unsupervised learning classification framework for cardiovascular diagnosis
In this work, a novel meta-heuristic-based feature ranking and classification approach is
developed on the real-time ECG data. Initially, the data is captured using AD8232 …
developed on the real-time ECG data. Initially, the data is captured using AD8232 …
[PDF][PDF] An outlier detection and feature ranking based ensemble learning for ECG analysis
VA Ardeti, VR Kolluru… - Int. J. Adv. Comput …, 2022 - pdfs.semanticscholar.org
Automated classification of each heartbeat class from the ECG signal is important to
diagnose cardiovascular diseases (CVDs) more quickly. ECG data acquired from the …
diagnose cardiovascular diseases (CVDs) more quickly. ECG data acquired from the …
Spectro-morphological Feature-based Machine Learning Approach for Grape Leaf Variety Classification
Artificial intelligence plays a major role in the advancement of viticulture, whether in wine
production or in grapevine's health monitoring. When the scale of the problem is large and …
production or in grapevine's health monitoring. When the scale of the problem is large and …
An advantage using feature selection with a quantum annealer
A Vlasic, H Grant, S Certo - arXiv preprint arXiv:2211.09756, 2022 - arxiv.org
Feature selection is a technique in statistical prediction modeling that identifies features in a
record with a strong statistical connection to the target variable. Excluding features with a …
record with a strong statistical connection to the target variable. Excluding features with a …
Feature selection through quantum annealing
A Vlasic, H Grant, S Certo - The Journal of Supercomputing, 2025 - Springer
Feature selection is a technique in statistical prediction modeling that identifies features in a
record with a strong statistical connection to the target variable. Excluding features with a …
record with a strong statistical connection to the target variable. Excluding features with a …