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 …
Benchmark of filter methods for feature selection in high-dimensional gene expression survival data
A Bommert, T Welchowski, M Schmid… - Briefings in …, 2022 - academic.oup.com
Feature selection is crucial for the analysis of high-dimensional data, but benchmark studies
for data with a survival outcome are rare. We compare 14 filter methods for feature selection …
for data with a survival outcome are rare. We compare 14 filter methods for feature selection …
[HTML][HTML] Benchmark for filter methods for feature selection in high-dimensional classification data
Feature selection is one of the most fundamental problems in machine learning and has
drawn increasing attention due to high-dimensional data sets emerging from different fields …
drawn increasing attention due to high-dimensional data sets emerging from different fields …
[PDF][PDF] Novel Optimized Feature Selection Using Metaheuristics Applied to Physical Benchmark Datasets.
In data mining and machine learning, feature selection is a critical part of the process of
selecting the optimal subset of features based on the target data. There are 2n potential …
selecting the optimal subset of features based on the target data. There are 2n potential …
Boosted sooty tern optimization algorithm for global optimization and feature selection
Feature selection (FS) represents an optimization problem that aims to simplify and improve
the quality of highly dimensional datasets through selecting prominent features and …
the quality of highly dimensional datasets through selecting prominent features and …
A systematic review of emerging feature selection optimization methods for optimal text classification: the present state and prospective opportunities
Specialized data preparation techniques, ranging from data cleaning, outlier detection,
missing value imputation, feature selection (FS), amongst others, are procedures required to …
missing value imputation, feature selection (FS), amongst others, are procedures required to …
[HTML][HTML] Artificial neural networks for the prediction of biochar yield: a comparative study of metaheuristic algorithms
In this study, an integrated framework of artificial neural networks (ANNs) and metaheuristic
algorithms have been developed for the prediction of biochar yield using biomass …
algorithms have been developed for the prediction of biochar yield using biomass …
A comparative study of machine learning methods for bio-oil yield prediction–A genetic algorithm-based features selection
A novel genetic algorithm-based feature selection approach is incorporated and based on
these features, four different ML methods were investigated. According to the findings, ML …
these features, four different ML methods were investigated. According to the findings, ML …
Ensemble modeling of landslide susceptibility using random subspace learner and different decision tree classifiers
In this study, we have developed five spatially explicit ensemble predictive machine learning
models for the landslide susceptibility mapping of the Van Chan district of the Yen Bai …
models for the landslide susceptibility mapping of the Van Chan district of the Yen Bai …
Fuzzy-based hunger games search algorithm for global optimization and feature selection using medical data
Feature selection (FS) is one of the basic data preprocessing steps in data mining and
machine learning. It is used to reduce feature size and increase model generalization. In …
machine learning. It is used to reduce feature size and increase model generalization. In …