Convert non-binary value into binary value? I'm kind of a newbie in ML

I have a dataset like:

282,4,1,1,0,0,1,1,41,0,1,1,1,0,0,0,0,0,0,0,0,1,0,8,4
282,5,1,1,0,1,1,2,41,0,1,1,1,0,0,0,0,0,0,3,1,2,0,12,4
161,5,2,1,0,0,1,1,41,0,1,0,0,0,0,0,0,0,0,3,1,4,0,3,5
282,4,2,1,0,32,1,3,41,0,1,0,0,0,0,0,0,0,0,0,0,1,0,4,1
0,3,1,1,0,32,1,1,32,1,1,1,1,0,0,0,0,0,0,0,0,4,0,8,4
0,4,2,5,0,0,1,1,32,0,1,1,1,0,0,0,0,0,0,0,0,1,0,2,5
....


With output is the last column, it has 6 outputs {A,B,C,D,E,F} reference to {0,1,2,3,4,5}. I'm using NN and SVM(C-SVC, NuSVC) for training this dataset, but the accuracy still low (>60%).

I want to try something else to improve the performance of current results so I plan to train 6 binary models like {A, not_A}, {B, not_B}...

Should I only change the output into binary and keep all features for training?

Or I need to change all features into binary too? If yes, can you give a short explanation for me?

Thanks for all your help!
What I understood from your question is, for your OneVsAll classifiers, is it ok to convert feature set in to binary or not?


  If this is correct, you should not convert your features into binary.
  From the shared data set, it seems you have continuous data set and if
  you convert into binary one, it will become categorical data, which
  will have different model representation. So according to me you
  should consume your data as it's. Though you can perform data
  normalization.
  
  Note: Multinominal Logistic Regression works on the similar principal
  i.e. OneVsAll. So you can try this one as well.