I'm building a neural net to predict the value of a piece of art with a wide range of inputs (size, art medium, etc.) and I would like to include the author as an input as well (it is often a huge factor in the value of a single piece of art).
My current concern is that the name of the author isn't an ideal numerical input for a NN (i.e. If I just code each author with an increasing integer value I will be indirectly assigning more value to authors further down the list -_-). My thoughts were to create separate inputs for all the authors in my data set and then just use one hot encoding to better represent the input to the NN.
This approach however runs into a problem when an author that is not included in my training data is used as an input to the NN (i.e. a new author). I can get around this with an "other author" input field but I am worried that this won't be accurate as I would not have trained the the NN for this input (all pieces of art with a valuation have an author).
I haven't fully thought this through but I thought of perhaps training 2 NN's, one for a valuation without an author and one for valuation with an author to ensure I have enough training data for an "authorless valuation" to still be reasonably accurate.
I am still trying to conceptualize the best NN architecture before I get stuck into the implementation so if anyone has any suggestions/comments I would be very grateful!
Thanks in advance, Vince
P.S. I am doing this as a small competition with a friend to test a NN vs the traditional commercial valuation techniques. Please help me get a win for Computer Science over Actuarial Science.