Table 9.

Advantages and disadvantages of feature selection methods.

Algorithms	Advantages	Disadvantages
GA [292]	Tries to avoid becoming stuck in a local optimal solution	GA does not guarantee an optimal solution and has high computational cost
mRMR [293]	Effectively reduces the redundant features while keeping the relevant features	Mutual information is incompatible with continuous data
LASSO [294]	Very accurate prediction, reduces overfitting, and improves model interpretability	In terms of independent risk factors, the regression coefficients may not be consistently interpretable
SFFS [295]	Reduces the number of nesting issues and unnecessary features	Difficult to detect all subsets
PCA [296]	Selects a number of important individuals from all the feature components, reduces the dimensionality of the original samples, and improves the classification accuracy	Only considers the linear relationships and interaction between variables at a higher level
WONN-MLB [288]	Integrates the maximum relevancy and minimum redundancy	Has certain amount of irrelevant attributes
HSOGR [90]	Effectively selects optimized features	Its execution is complex