Suppose I have a neural network with 5 inputs: [A,B,C,D,E]
There is only 1 output. The expected accuracy of the model should increase when all 5 inputs are available, but often not all 5 inputs are available. For example, I might have case where I only have a variable number of the inputs, e.g. [A,B,C,-,-], [A,-,-,-,E], [-,B,-,D,-], [A,-,-,-,-], [-,-,C,-,-], [A,-,C,D,E], etc.
In such a situation, what is the best way to train or build the neural network? Are there any specific approaches or architectures recommended for this type of problem?
A couple ideas that come to mind include:
Double the number of inputs to the neural network by including a second "binary input vector" that determines whether the input variable is present or not. For example, the binary input vector for the inputs
[A,-,C,-,E]would simply correspond to[1,0,1,0,1], which could be fed into the neural network as well. The outstanding question is how does one treat the undefined variables with "-" as placeholders in such an example...perhaps defaulting to 0 for "-" is one naive but simple way when coupling the binary vector.Build and train separate neural network for every combination of
[A,B,C,D,E]— this could certainly be implemented, but would be a brute force approach that requires a lot of training and be rather inefficient. For 5 variables, this would require 31 separate neural networks, and would scale poorly as the number of potential input variables increase
Any and all insights into this problem are appreciated!
