An alternative for one-hot encoding in neural network models
Lazar Zlatić
This paper proposes an algorithm that implements binary encoding of the
categorical features of neural network model input data, while also
implementing changes in the forward and backpropagation procedures in order to
achieve the property of having model weight changes, that result from the
neural network learning process for certain data instances of some feature
category, only affect the forward pass calculations for input data instances of
that same feature category, as it is in the case of utilising one-hot encoding
for categorical features.