I am trying to make predictions (using Weka) on a tabular dataset. It is a
categorical dataset which is encoded by
I got a good result for SVM and Logistic Regression, namely the accuracy is around 85%.
The dataset is high-dimensional and I like to fine-tune my accuracy.
So, I am thinking about the feature selection method. I found different feature selection techniques, such as
Classifier Attribute eval,
classifier subset eval,
Cv attribute eval,
Gain ratio attribute eval,
Info gain attribute eval,
relief f attribute eval,
Wrapper subset eval.
I would like to know which one would be the best for the dataset that shows good accuracy with Logistic Regression or SVM?