KLexp (version 1.0.0)

kernel_lasso_expansion: 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.

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,<U+201D> 2021 IEEE. About Kernel-lasso feature expansion method: boosting the prediction ability of machine learning in heart attack<U+201D> 2021 IEEE.

Examples

# NOT RUN {
##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

# }