kernel_lasso_expansion {KLexp} | R Documentation |
kernel_lasso_expansion
Description
Kernel_lasso is one feature selection method, which combines the feature expansion and lasso regression together. Kernel function will increase the dimensions of the existed data and then reduce the features by lasso. 'glmnet' package should be higher than 4.1-2.
Arguments
x |
Your input features, which have to be data.frame with at least two variables. |
y |
The dependent variable |
sigma |
The hyperparameter of RBF kernel function, which indicates the width. |
dataframe |
Wether the data is dataframe. The default is TURE |
standard |
Using 'max_min_scale' or 'Z_score' method to standardize the data. NULL means no standardization |
Value
The result is stored in one list which contains the orignial dataset, amplified dataset, final features, and lasso output.
Author(s)
Zongrui Dai
Source
https://github.com/Zongrui-Dai/Kernel-lasso-feature-expansion
References
Z. Dai, J. Li, T. Gong, C. Wang (2021), Kernel_lasso feature expansion method: boosting the prediction ability of machine learning in heart attack,” 2021 IEEE. About Kernel-lasso feature expansion method: boosting the prediction ability of machine learning in heart attack” 2021 IEEE.
Examples
##Regression (MSE)
data(attenu,package = 'datasets')
result<-kernel_lasso_expansion(x=attenu[,-c(3,5)],y=attenu[,5],
standard = 'max_min',sigma=0.01,control = lasso.control(nfolds=3,type.measure = 'mse'))
summary(result)
#Plot the lasso
plot(result$lasso)
#Result
result$original ##The original feature space
result$expansion ##The feature space after expansion
result$final_feature ##The name of the final feature
result$final_data ##The dataframe of final feature