svm_imp: SVM feature importance

In kerntools: Kernel Functions and Tools for Machine Learning Applications

SVM feature importance

Description

Recovering the features importances from a SVM model.

Usage

svm_imp(
  X,
  svindx,
  coeff,
  result = "absolute",
  cos.norm = FALSE,
  center = FALSE,
  scale = FALSE
)

Arguments

X	Matrix or data.frame that contains real numbers ("integer", "float" or "double"). X is NOT the kernel matrix, but the original dataset used to compute the kernel matrix.
svindx	Indices of the support vectors.
coeff	target * alpha.
result	A string. If "absolute", the absolute values of the importances are returned. If "squared", the squared values are returned. Any other input will result in the original (positive and/or negative) importance values (see Details). (Defaults: "absolute").
cos.norm	Boolean. Was the data cosine normalized prior to training the model? (Defaults: FALSE).
center	Boolean. Was the data centered prior to training the model? (Defaults: FALSE).
scale	Boolean. Was the data scaled prior to training the model? (Defaults: FALSE).

Details

This function may be not valid for all kernels. Do not use it with the RBF, Laplacian, Bray-Curtis, Jaccard/Ruzicka, or Kendall's tau kernels unless you know exactly what you are doing.

Usually the sign of the importances is irrelevant, thus justifying working with the absolute or squared values; see for instance Guyon et al. (2002). Some classification tasks are an exception to this, when it can be demonstrated that the feature space is strictly nonnegative. In that case, a positive importance implies that a feature contributes to the "positive" class, and the same with a negative importance and the "negative" class.

Value

The importance of each feature (a vector).

References

Guyon, I., Weston, J., Barnhill, S., and Vapnik, V. (2002) Gene selection for cancer classification using support vector machines. Machine learning, 46, 389-422. Link

Examples

data1 <- iris[1:100,]
sv_index <- c( 24, 42, 58, 99)
coefficients <- c(-0.2670988, -0.3582848,  0.2129282,  0.4124554)
# This SV and coefficients were obtained from a model generated with kernlab:
# model <- kernlab::ksvm(Species ~ .,data=data1, kernel="vanilladot",scaled = TRUE)
# sv_index <- unlist(kernlab::alphaindex(model))
# coefficients <- kernlab::unlist(coef(model))
# Now we compute the importances:
svm_imp(X=data1[,-5],svindx=sv_index,coeff=coefficients,center=TRUE,scale=TRUE)

kerntools documentation built on April 3, 2025, 7:52 p.m.