Should boolean features be normalized and should false be -1 or 0 I am attempting to train an SVM from a set of features which are both numeric and categorical, for example:


Distance X (Numeric)
Distance Y (Numeric)
Font Size Difference (Numeric)
Word 1 Bold (Boolean)
Word 2 Bold (Boolean)
Word 1 Font Size (Numeric)
...


For mapping the features to a feature array I am treating true as 1 and false as 0 and then normalizing the entire feature array using Z-Scores.

Should I instead encode false as -1 so it has a numerical impact on the generation of the support vectors, with a value of 0 it does not modify the chosen vector (I assume)?

Should boolean features be normalized in the same way as numerics or should they be left with their encoded values?
Based on the comment by Ricardo Cruz I tried switching the value of false from 0 to -1 and also normalization on and off.

Neither switching the value from 0 to - 1 or normalization of features had any impact on the values predicted by the SVM. In this case I was using a Gaussian kernel and Sequential Minimal Optimization for my SVM.