An alternative for one-hot encoding in neural network models

Abstract

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.