A Two-Step Feature Selection Procedure to Handle High-Dimensional Data in Regression Problems
Explore this paper's citation graph
- Type
- article
- Published
- 2023-09-16
- Cited by
- 1
- References
- 27
- OpenAlex
- https://openalex.org/W4387870091
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:264456912
Keywords
Elastic net regularization, Feature selection, Computer science, Feature (linguistics), Artificial intelligence
References
- Feature Screening via Distance Correlation Learning
- The fused Kolmogorov filter: A nonparametric model-free screening method
- Model-Free Feature Screening for Ultrahigh Dimensional Data
- Prediction by Supervised Principal Components
- An Appraisal of Least Squares Programs for the Electronic Computer from the Point of View of the User
- Hedonic housing prices and the demand for clean air
- Regression Diagnostics: Identifying Influential Data and Sources of Collinearity
- Robust rank correlation based screening
- A Statistical View of Some Chemometrics Regression Tools
- Ultrahigh Dimensional Variable Selection: beyond the linear model
- Regulation of gene expression in the mammalian eye and its relevance to eye disease
- Regression Shrinkage and Selection via the Lasso
- High-Dimensional Statistics with a View Toward Applications in Biology
- Discussion of “Sure Independence Screening for Ultra-High Dimensional Feature Space
- Strong Sure Screening of Ultra-high Dimensional Categorical Data
- Regression Diagnostics: Identifying Influential Data and Sources of Collinearity
- An ACO–ANN based feature selection algorithm for big data
- A Nested Genetic Algorithm for feature selection in high-dimensional cancer Microarray datasets
- Informative Gene Selection for Microarray Classification via Adaptive Elastic Net with Conditional Mutual Information
- A two-layer feature selection method using Genetic Algorithm and Elastic Net
Cited by
Related papers
- Comparison of Feature Selection Methods Based on Lasso
- Cluster feature selection in high-dimensional linear models
- Elastic Net based Feature Ranking and Selection
- Ensemble Feature Selection Methods for a Better Regularization of the Lasso Estimate in P >> N Gene Expression Datasets
- Extraction of Important Factors in a High-Dimensional Data Space: An Application for High-Growth Firms
- Efficiently handling feature redundancy in high-dimensional data