Ranked-categorical variables in Artificial Neural Networks? As far as I understand, input variables in Artificial Neural Networks (ANN) must be continuous, right? (And there are a number of methods to convert categorical to continuous variables described in the literature.) However, I wonder if ranked-categorical variables (e.g., with classes 1, 2, and 3, where is 1 = low, 2 = average, 3 = high) should also be transformed into continuous variables before they can be included into an ANN?
 A: Categorical features can be nominal (no ordering, like race, gender, religion, etc.), or ordinal (ranked, like in Likert scale). 
The method to feed in nominal variables is by one-hot encoding, which is straight-forward. So in the case you have a Colour feature consisting of Red, Blue, Green, you would just create 3-1 binary features to represent the feature. Here 3 is the cardinality of your nominal feature. There are readily available pre-processing functions in many languages that do this for you, e.g. in Python.
For ordinal variables, you could still feed in the feature using one-hot encoding. However, this would come at the expense of losing the ranking in between the feature values. For instance movie review as a feature can have the following values rubbish, bad, mediocre, good, sensational.  In this case, you are better of using label-encoding, where you map each category to, e.g. an integer, in a way that preserves the distances between feature values.
Finally, in answer to your question, the mapped values from label-encoding, which you would feed into neural networks or GLM's in general, do not need to be (and indeed cannot be) continuous.
A: Inputs to neural networks do not have to be continuous. Other examples are binary or integer variables.
You can certainly use ordinal variables too.
