You may try PROC QUANTSELECT to perform Support Vector Machine (SVM) classification with LASSO penalty. An example is as follows:
/* Start: data simulation */
%let seed=111;
Data raw;
array x[10];
do i=1 to 1000;
x0=1; /* regressor for estimating bias parameter */
do j=1 to 10;
x
end;
y = 2*((3*x1+2*x2+x3+1+0.1*rannor(&seed))>0)-1; /*So the true model is proportional to (bias=1, x1=3, x2=2, x1=1).*/
output;
end;
run;
/* End: data simulation */
/* TransformSVM2QR macro pre-processes raw data for using QUANTSELECT.*/
%macro TransformSVM2QR(raw);
data transferred_&raw;
set &raw;
%do j=1 %to 10;
x&j = y*x&j;
%end;
bias=y;
y=1;
run;
%mend TransformSVM2QR;
%TransformSVM2QR(raw);
ods graphics on;
proc quantselect data=transferred_raw plot=all;
model y= bias x1-x10/quantile=1 noint selection=lasso(choose=validate include=1 sh=3);
partition fraction(validate=0.2);
run;
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