Hi there, I am using a dataset called KDD 99 to feed into a neural network. I also read some paper about this dataset. In this data, there are some nominal variables and some numerical values. The papers that I read recommended to preprocess the data as follow:

+ nominal variables (some have more than 60 different values) -> encode to integers (0,1,2,3,4 ...)

+ numerical variables: - After encoding the nominal variables, my data now has all the numbers. - Most of them are binary 0 and 1. - Some have range [0-256] - Few have range [0 - 1 billion]

And the paper SCALE the variables within range [0-256] to the range [0-1] range [0-1billion] to [0-10] by Logarithmic scaling.

My questions are: Q1: Why do we have to encode the nominal data into integers? A nominal value A is encoded to 1 and B to 2, but A is not less than B (1<2). I suspect that this is because we want to speed up the learning of the NN.

Q2: Is that true that the imbalance in the numerical data (range [0-256] and range [0-1billion]) will affect the learning process? Do we have to do such processing?

Q3: If I have to do such preprocessings, how could I scale [0-256] to [0-1] by NORMAL SCALING? Is that true I just divide every values with the maximum values?

Q4: How can I use LOGARITHMIC SCALING to scale [0-1 billion] to [0-1] in SAS?

Thanks Have a nice weekend. Patrick Tran

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