To answer this particular question:
"Is there any way instead of selecting category choices like this to select initial features? For example, I have features Weight, Model_Year, Color, Owner_Name, and Owner_Country and I want to know which of these are most predictive, not which of their category choices are."
I don't know why you would want to do this because selecting categories is in principle more useful than selecting predictors. But if you insist on doing that, just dummify variables inside your my_fun_lib. This works as long as all your features are categorical. Here is an example:
%%Form a table with two categorical variables
load fisheriris
T = table(categorical(meas(:,1)),categorical(meas(:,2)));
%%Convert table into matrix
X = zeros(size(T));
for p=1:size(X,2)
X(:,p) = double(T{:,p});
end
%%Turn into a binary classification problem
y = strcmp(species,'setosa');
%%Run sequentialfs
c = cvpartition(y,'k',5);
opts = statset('display','iter');
inmodel = sequentialfs(@mycrit,X,y,'cv',c,'options',opts);
where mycrit looks like this:
function val = mycrit(Xtrain,Ytrain,Xtest,Ytest)
Ntrain = size(Xtrain,1);
X = dummyvar([Xtrain; Xtest]);
Xtrain = X(1:Ntrain,:);
Xtest = X(Ntrain+1:end,:);
obj = fitcsvm(Xtrain,Ytrain);
val = loss(obj,Xtest,Ytest);
end
