Question

I am working on an ANN using Backpropagation at the moment, for a classification task. I am however a little confused about normalizing the data set I am using(I do not have a strong stats/probability background).

A sample of the data is shown below:

5.1, 3.5, 1.4, 0.2, 1
5.2, 2.7, 3.9, 1.4, 2
5.9, 3.0, 5.1, 1.8, 3

Where the last token of each is the class.

Now, as I am using the Sigmoid transfer function, my network obviously cannot output a value greater than 1, so the data needs to be normalized/scaled.

My first question; would I need to scale both the features and the class, or just the class?

My second question, is there any ‘de-facto’ or commonly used method of doing such scaling?

Regards,
Jack Hunt

Answer 1

It’s usually recommended to also scale the features. Scaling should be as straightforward as scaled_feature = (feature – min(featurearray))/(max(featurearray) – min(featurearray)).

So for the first attribute column, the new data would be:
(5.1-5.1)/(5.9-5.1);
(5.2-5.1)/(5.9-5.1);
(5.9-5.1)/(5.9-5.1)